Diagnostic analyzer for content receiver using wireless execution device

ABSTRACT

Techniques described herein relate to performing wireless diagnostic analyses including execution and evaluations of interactive content resources executed by execution devices on and/or for separate content receiver devices. A multi-phrase diagnostic session may proceed with an execution of an initial set of diagnostic modules on an execution device, during which interactive content is transmitted/received from a connected receiver device. The results of the diagnostic modules may be evaluated in real-time and transmitted to a diagnostic analyzer server to select additional diagnostic modules for execution during the diagnostic session. The diagnostic analyzer server may select the additional diagnostic modules based on based on response data received via the content execution device to the interactive content of the previously executed diagnostic modules, and/or data received from additional data sources related to the content receiver.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/638,018 entitled “DIAGNOSTIC ANALYZER FOR CONTENT RECEIVER USINGWIRELESS EXECUTION DEVICE” and filed on Jun. 29, 2017.

BACKGROUND

Using content delivery network infrastructures and content receiverdevices such as laptop and desktop client computers, tablet devices,televisions, etc., upstream content providers can provide vast anddiverse content resources to users live, live-streamed, and on-demand.In some cases, content distribution networks and systems may generateand provide various interactive content resources to content receiverdevices using various delivery techniques. Such interactive contentresources may include, for example, audio and video media content,gaming software, professional training and educational contentresources, clinical assessments administered by educators or medicalpersonnel to students or patients, and the like. In some cases, thedevices executing and providing the content resources to users mayreceive and analyze responses and other feedback data associated withthe execution of the content resources, and may return such data to thecontent provider via one or more feedback channels. For example, thecontent provider may receive feedback from a content receiver device onthe content or the quality of the content delivery. Additional feedbackmay correspond to the execution status of resource(s) on the contentreceiver device, while other feedback may relate to interactive userresponses provided during or after execution of content resources.

BRIEF SUMMARY

Various techniques are described herein for performing wirelessdiagnostic analyses including execution and evaluations of interactivecontent resources executed by execution devices on and/or for separatecontent receiver devices. In some embodiments, multi-phrase diagnosticsessions may include executions of successive sets of diagnostic moduleson interactive content execution devices. During the execution of thediagnostic modules, an interactive content execution device may transmitdata to and/or receive data from one or more interactive contentreceiver devices connected to the interactive content execution device.The results of diagnostic modules may be evaluated and/or scored by theexecution device, and then transmitted to a diagnostic analyzer server.The diagnostic analyzer server may analyze the results and selectadditional diagnostic modules for execution during the same diagnosticsession and/or subsequent diagnostic sessions. Such analyses may bebased on receiver response/performance data received via the executiondevice to the previously executed diagnostic modules, along with datareceived from additional data sources related to the content receiver.

In accordance with certain embodiments described herein, wirelessinteractive diagnostic systems may be implemented including a diagnosticanalyzer server, an interactive content execution device, and one ormore interactive content receiver devices. Wireless connections may beestablished between the interactive content execution device and thereceiver devices, for example, Bluetooth connections or othershort-range wireless connections, while the interactive contentexecution device may communicate with the diagnostic analyzer server viaa separate secure connection over a packet-switched network. Interactivecontent execution devices may be configured to receive selections ofdiagnostic modules comprising interactive content resources, and theninitiate execution of the diagnostic modules for a particular contentreceiver device. During the execution of the diagnostic modules, aninteractive content execution device may transmit interactive content toand receive response data from the particular content receiver device.The content execution device may analyze the results/responses to theinteractive content, evaluate and/or score the performance of thereceiver, and then transmit the evaluation/score to the diagnosticanalyzer server. The diagnostic analyzer server may analyze theevaluations/scores in real-time or near real-time and select additionaldiagnostic modules for the receiver. The additional diagnostic modulesmay be transmitted back to the content execution device, which mayinitiate the execution and transmit the additional interactive contentto the content receiver devices.

Additional techniques described herein relate to the analyses andselection of additional diagnostic modules by the diagnostic analyzerdevice. In some cases, such selection may happen in real-time during adiagnostic session between a content executor and content receiver. Asnoted above, the diagnostic analyzer device may receive sets ofevaluations/scores from a content execution device, based on theresponses to the interactive content received from content receiverdevices. In some embodiments, the diagnostic analyzer device may alsoretrieve/receive additional previously collected entity recordsassociated with the recipient of the interactive content. The diagnosticanalyzer device then may determine one or more subsequent diagnosticmodules for particular recipients of the interactive content, based onthe received performance measurements and the previously collectedentity records associated with the particular interactive contentrecipient. The selection of subsequent diagnostic modules may include,for example, determining multiple possible diagnoses for particularinteractive content recipients, ranking the multiple possible diagnosesfor probability/likelihood, based on analytics data correlatingprobabilities of diagnoses to evaluation results/scores of a previouslyexecuted set of diagnostic modules. The determined probabilities may bebased on predetermined statistical data and/or trained machine learningalgorithms. After determining the highest probability diagnosis (ormultiple high-probability diagnoses), the additional diagnostic modulesmay be selected based on analyses of a library of potential additionaldiagnostic modules, and calculations of the amounts of change to thediagnostic probabilities that are projected to result from the executionof the particular additional diagnostic modules. For instance,diagnostic modules that are projected to significantly change thediagnostic probabilities of one or more of the high-probabilitydiagnoses may be selected over additional diagnostic modules that areprojected to result in less or no change to the diagnosticprobabilities. Finally, there may be subsequent diagnostic modules maybe selected by and/or transmitted to the interactive content executiondevice, from the diagnostic analyzer device, to be executed and providedto the interactive content recipient.

Further techniques and areas of applicability of the present disclosurewill become apparent from the detailed description provided hereinafter.It should be understood that the detailed description and specificexamples, while indicating various embodiments, are intended forpurposes of illustration only and are not intended to necessarily limitthe scope of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example content distributionnetwork in accordance with one or more embodiments of the disclosure.

FIG. 2 is a block diagram illustrating a computer server and computingenvironment within a content distribution network, in accordance withone or more embodiments of the disclosure.

FIG. 3 is a block diagram illustrating an embodiment of one or more datastore servers of a content distribution network, in accordance with oneor more embodiments of the disclosure.

FIG. 4 is a block diagram illustrating an embodiment of one or morecontent management servers within a content distribution network, inaccordance with one or more embodiments of the disclosure.

FIG. 5 is a block diagram illustrating the physical and logicalcomponents of a special-purpose computer device within a contentdistribution network, in accordance with one or more embodiments of thedisclosure.

FIG. 6 is a block diagram illustrating a communication network inaccordance with one or more embodiments of the disclosure.

FIG. 7 is a block diagram illustrating an example diagnostic analysisand selection system for interactive content resources, according to oneor more embodiments of the disclosure.

FIG. 8 is a swim lane diagram illustrating an example process ofexecuting, analyzing, and selecting diagnostic modules includinginteractive content resources, for a particular content receiver,according to one or more embodiments of the disclosure.

FIG. 9 is a flow diagram illustrating an example process of monitoringand evaluating content receivers, using diagnostic modules executed on acontent execution device, according to one or more embodiments of thedisclosure.

FIG. 10 is a flow diagram illustrating an example process of analyzingthe execution results of diagnostic modules, and selecting additionaldiagnostic modules for a particular content receiver, according to oneor more embodiments of the disclosure.

FIG. 11 is an example user interface display screen outputting aninitial assessment battery of interactive content resources for aparticular content receiver, according to one or more embodiments of thedisclosure.

FIGS. 12A and 12B are example mappings between a plurality ofcharacteristics and a plurality of diagnoses used for assessmentanalysis, diagnosis, and selection of subsequent diagnostic modules,according to one or more embodiments of the disclosure.

FIG. 13 is an example user interface display screen outputtingdiagnostic profile data corresponding to a particular user, according toone or more embodiments of the disclosure.

FIG. 14 is an example user interface display screen outputtingadditional assessments of interactive content resources selected for aparticular content receiver by a diagnostic analyzer server, accordingto one or more embodiments of the disclosure.

DETAILED DESCRIPTION

The ensuing description provides illustrative embodiment(s) only and isnot intended to limit the scope, applicability or configuration of thedisclosure. Rather, the ensuing description of the illustrativeembodiment(s) will provide those skilled in the art with an enablingdescription for implementing a preferred exemplary embodiment. It isunderstood that various changes can be made in the function andarrangement of elements without departing from the spirit and scope asset forth in the appended claims.

Embodiments of the present disclosure may be performed within contentdistribution networks implemented to transmit/receive content such astraining, entertainment, assessment, and evaluation content, among manyother types of content. Content can be delivered on-demand to devicesoperated by users in remote or local locations or can be delivered livein a present local or live-streamed to remote locations. In some cases,client execution devices and/or users receiving the content may providefeedback relating to the execution of the content. In this context,various techniques (e.g., systems, methods, computer-program productstangibly embodied in a non-transitory machine-readable storage medium,etc.) are described herein relating to performing wireless diagnosticanalyses including execution and evaluations of interactive contentresources executed by execution devices on and/or for separate contentreceiver devices. For example, techniques described herein may includeanalyzing the execution of content resources, which may be initiated andmonitored execution client devices configured to transmit interactivecontent to separate receiver client devices. Additionally, the executionclient devices may communicate with a diagnostic analyzer to select andretreive sets of interactive content resources in the form of diagnosticmodules (e.g., assessments) for execution on particular executiondevices (e.g., for particular receiver devices) based on such analyses.In some embodiments, content resource execution data, corresponding tothe responses and/or feedback received from the receiver devices, may bereceived from execution devices on which content resources have beenexecuted and provided to end users via the receiver client devices. Suchinteractive content resources may include, for example, audio and videomedia resources, gaming software resources, professional training andeducational resources, clinical assessment resources, and the like. Insome cases, the interactive content resources may be in the form ofdiagnostic software modules designed to perform diagnostic consultingtasks for clinicians when classifying and diagnosing examinee users(e.g., individual students, patients, trainees, gamers, etc.) Responseor feedback data corresponding to the execution of content resources viaexecution devices may be received and stored in one or more datastructures storing associations between particular content resources andparticular examinees (and/or particular content executors). The datastructures may be analyzed to determine correlations between particularcharacteristics, skills, or traits of an examinee, and potentialdiagnoses that may apply to the examineed. After determining suchcorrelations, content execution devices and/or diagnostic analyzerservers may select additional interactive content resources to continuea diagnostic assessment of the content receiver/examinee, and theselections may be provided to content execution device during a contentexecution session following an authenticated login by the contentexecutor and/or the content receiver.

In some embodiments, multi-phase diagnostic sessions may includeexecutions of successive sets of diagnostic modules on interactivecontent execution devices. During the execution of the diagnosticmodules, an interactive content execution device may transmit data toand/or receive data from interactive one or more interactive contentreceiver devices connected to the interactive content execution device.The results of diagnostic modules may be evaluated and/or scored by theexecution device, and then transmitted to a diagnostic analyzer server.The diagnostic analyzer server may analyze the results and selectadditional diagnostic modules for execution during the same diagnosticsession and/or subsequent diagnostic sessions. Such analyses may bebased on receiver response/performance data received via the executiondevice to the previously executed diagnostic modules, along with datareceived from additional data sources related to the content receiver.

For example, a wireless interactive diagnostic system may be implementedwithin a computing environment including a diagnostic analyzer server,an interactive content execution device, and one or more interactivecontent receiver devices. Wireless connections may be establishedbetween the interactive content execution device and the receiverdevices, for example, Bluetooth connections or other short-rangewireless connections, while the interactive content execution device maycommunicate with the diagnostic analyzer server via a separate secureconnection over a packet-switched network. Interactive content executiondevices may be configured to receive selections of diagnostic modulescomprising interactive content resources, and then initiate execution ofthe diagnostic modules for a particular content receiver device. Duringthe execution of the diagnostic modules, an interactive contentexecution device may transmit interactive content to and receiveresponse data from the particular content receiver device. The contentexecution device may analyze the results/responses to the interactivecontent, evaluate and/or score the performance of the receiver, and thentransmit the evaluation/score to the diagnostic analyzer server. Thediagnostic analyzer server may analyze the evaluations/scores inreal-time or near real-time and select additional diagnostic modules forthe receiver. The additional diagnostic modules may be transmitted backto the content execution device, which may initiate the execution andtransmit the additional interactive content to the content receiverdevices.

Additional techniques described herein relate to the analyses andselection of additional diagnostic modules by the diagnostic analyzerdevice. In some cases, such selection may happen in real-time during adiagnostic session between a content executor and content receiver. Asnoted above, the diagnostic analyzer device may receive sets ofevaluations/scores from a content execution device, based on theresponses to the interactive content received from content receiverdevices. In some embodiments, the diagnostic analyzer device may alsoretrieve/receive additional previously collected entity recordsassociated with the recipient of the interactive content. The diagnosticanalyzer device then may determine one or more subsequent diagnosticmodules for particular recipients of the interactive content, based onthe received performance measurements and the previously collectedentity records associated with the particular interactive contentrecipient. The selection of subsequent diagnostic modules may include,for example, determining multiple possible diagnoses for particularinteractive content recipients, ranking the multiple possible diagnosesfor probability/likelihood, based on analytics data correlatingprobabilities of diagnoses to evaluation results/scores of a previouslyexecuted set of diagnostic modules. The determined probabilities may bebased on predetermined statistical data and/or trained machine learningalgorithms. After determining the highest probability diagnosis (ormultiple high-probability diagnoses), the additional diagnostic modulesmay be selected based on analyses of a library of potential additionaldiagnostic modules, and calculations of the amounts of change to thediagnostic probabilities that are projected to result from the executionof the particular additional diagnostic modules. For instance,diagnostic modules that are projected to significantly change thediagnostic probabilities of one or more of the high-probabilitydiagnoses may be selected over additional diagnostic modules that areprojected to result in less or no change to the diagnosticprobabilities. Finally, the may be subsequent diagnostic modules may beselected by and/or transmitted to the interactive content executiondevice, from the diagnostic analyzer device, to be executed and providedto the interactive content recipient.

Further embodiments may produce segmented outputs illustratinglikelihood and/or probability for the presence or absence of specificexpressions of relevant clinical disorders for the content recipient tobe utilized as targets for intervention of accommodation. Further,expressions of behavioral disturbances not specifically associated withmost probable diagnosis may be identified due to potential forinterference in application of interventions and/or accommodations ordue to the high probability of interference with examinees capacity forexpected levels of functioning in relevant settings.

With reference now to FIG. 1, a block diagram is shown illustratingvarious components of a content distribution network (CDN) 100 whichimplements and supports certain embodiments and features describedherein. In some embodiments, the content distribution network 100 cancomprise one or several physical components and/or one or severalvirtual components such as, for example, one or several cloud computingcomponents. In some embodiments, the content distribution network 100can comprise a mixture of physical and cloud computing components.

Content distribution network 100 may include one or more contentmanagement servers 102. As discussed below in more detail, contentmanagement servers 102 may be any desired type of server including, forexample, a rack server, a tower server, a miniature server, a bladeserver, a mini rack server, a mobile server, an ultra-dense server, asuper server, or the like, and may include various hardware components,for example, a motherboard, a processing units, memory systems, harddrives, network interfaces, power supplies, etc. Content managementserver 102 may include one or more server farms, clusters, or any otherappropriate arrangement and/or combination or computer servers. Contentmanagement server 102 may act according to stored instructions locatedin a memory subsystem of the server 102, and may run an operatingsystem, including any commercially available server operating systemand/or any other operating systems discussed herein.

The content distribution network 100 may include one or more data storeservers 104, such as database servers and file-based storage systems.The database servers 104 can access data that can be stored on a varietyof hardware components. These hardware components can include, forexample, components forming tier 0 storage, components forming tier 1storage, components forming tier 2 storage, and/or any other tier ofstorage. In some embodiments, tier 0 storage refers to storage that isthe fastest tier of storage in the database server 104, andparticularly, the tier 0 storage is the fastest storage that is not RAMor cache memory. In some embodiments, the tier 0 memory can be embodiedin solid state memory such as, for example, a solid-state drive (SSD)and/or flash memory.

In some embodiments, the tier 1 storage refers to storage that is one orseveral higher performing systems in the memory management system, andthat is relatively slower than tier 0 memory, and relatively faster thanother tiers of memory. The tier 1 memory can be one or several harddisks that can be, for example, high-performance hard disks. These harddisks can be one or both of physically or communicatively connected suchas, for example, by one or several fiber channels. In some embodiments,the one or several disks can be arranged into a disk storage system, andspecifically can be arranged into an enterprise class disk storagesystem. The disk storage system can include any desired level ofredundancy to protect data stored therein, and in one embodiment, thedisk storage system can be made with grid architecture that createsparallelism for uniform allocation of system resources and balanced datadistribution.

In some embodiments, the tier 2 storage refers to storage that includesone or several relatively lower performing systems in the memorymanagement system, as compared to the tier 1 and tier 2 storages. Thus,tier 2 memory is relatively slower than tier 1 and tier 0 memories. Tier2 memory can include one or several SATA-drives or one or severalNL-SATA drives.

In some embodiments, the one or several hardware and/or softwarecomponents of the database server 104 can be arranged into one orseveral storage area networks (SAN), which one or several storage areanetworks can be one or several dedicated networks that provide access todata storage, and particularly that provides access to consolidated,block level data storage. A SAN typically has its own network of storagedevices that are generally not accessible through the local area network(LAN) by other devices. The SAN allows access to these devices in amanner such that these devices appear to be locally attached to the userdevice.

Data stores 104 may comprise stored data relevant to the functions ofthe content distribution network 100. Illustrative examples of datastores 104 that may be maintained in certain embodiments of the contentdistribution network 100 are described below in reference to FIG. 3. Insome embodiments, multiple data stores may reside on a single server104, either using the same storage components of server 104 or usingdifferent physical storage components to assure data security andintegrity between data stores. In other embodiments, each data store mayhave a separate dedicated data store server 104.

Content distribution network 100 also may include one or more userdevices 106 and/or supervisor devices 110. User devices 106 andsupervisor devices 110 may display content received via the contentdistribution network 100, and may support various types of userinteractions with the content. User devices 106 and supervisor devices110 may include mobile devices such as smartphones, tablet computers,personal digital assistants, and wearable computing devices. Such mobiledevices may run a variety of mobile operating systems, and may beenabled for Internet, e-mail, short message service (SMS), Bluetooth®,mobile radio-frequency identification (M-RFID), and/or othercommunication protocols. Other user devices 106 and supervisor devices110 may be general purpose personal computers or special-purposecomputing devices including, by way of example, personal computers,laptop computers, workstation computers, projection devices, andinteractive room display systems. Additionally, user devices 106 andsupervisor devices 110 may be any other electronic devices, such as athin-client computers, an Internet-enabled gaming systems, business orhome appliances, and/or a personal messaging devices, capable ofcommunicating over network(s) 120.

In different contexts of content distribution networks 100, user devices106 and supervisor devices 110 may correspond to different types ofspecialized devices, for example, student devices and teacher devices inan educational network, employee devices and presentation devices in acompany network, different gaming devices in a gaming network,clinician/teacher devices and patient/student devices in a clinicaldiagnosis or learning classification network, etc. In some embodiments,user devices 106 and supervisor devices 110 may operate in the samephysical location 107, such as a classroom or conference room. In suchcases, the devices may contain components that support directcommunications with other nearby devices, such as a wirelesstransceivers and wireless communications interfaces, Ethernet sockets orother Local Area Network (LAN) interfaces, etc. In otherimplementations, the user devices 106 and supervisor devices 110 neednot be used at the same location 107, but may be used in remotegeographic locations in which each user device 106 and supervisor device110 may use security features and/or specialized hardware (e.g.,hardware-accelerated SSL and HTTPS, WS-Security, firewalls, etc.) tocommunicate with the content management server 102 and/or other remotelylocated user devices 106. Additionally, different user devices 106 andsupervisor devices 110 may be assigned different designated roles, suchas presenter devices, teacher devices, clinician devices, administratordevices, or the like, and in such cases the different devices may beprovided with additional hardware and/or software components to providecontent and support user capabilities not available to the otherdevices.

The content distribution network 100 also may include a privacy server108 that maintains private user information at the privacy server 108while using applications or services hosted on other servers. Forexample, the privacy server 108 may be used to maintain private data ofa user within one jurisdiction even though the user is accessing anapplication hosted on a server (e.g., the content management server 102)located outside the jurisdiction. In such cases, the privacy server 108may intercept communications between a user device 106 or supervisordevice 110 and other devices that include private user information. Theprivacy server 108 may create a token or identifier that does notdisclose the private information and may use the token or identifierwhen communicating with the other servers and systems, instead of usingthe user's private information.

As illustrated in FIG. 1, the content management server 102 may be incommunication with one or more additional servers, such as a contentserver 112, a user data server 112, and/or an administrator server 116.Each of these servers may include some or all of the same physical andlogical components as the content management server(s) 102, and in somecases, the hardware and software components of these servers 112-116 maybe incorporated into the content management server(s) 102, rather thanbeing implemented as separate computer servers.

Content server 112 may include hardware and software components togenerate, store, and maintain the content resources for distribution touser devices 106 and other devices in the network 100. For example, incontent distribution networks 100 used for professional training andeducational purposes, or clinical diagnosis of students/patents, thecontent server 112 may include data stores of training materials,presentations, plans, syllabi, reviews, evaluations, interactiveprograms and simulations, course models, course outlines, assessmentsand diagnostic modules, and various training interfaces that correspondto different materials and/or different types of user devices 106. Incontent distribution networks 100 used for media distribution,interactive gaming, and the like, a content server 112 may include mediacontent files such as music, movies, television programming, games, andadvertisements.

User data server 114 may include hardware and software components thatstore and process data for multiple users relating to each user'sactivities and usage of the content distribution network 100. Forexample, the content management server 102 may record and track eachuser's system usage, including their user device 106, content resourcesaccessed, and interactions with other user devices 106. This data may bestored and processed by the user data server 114, to support usertracking and analysis features. For instance, in the contexts ofprofessional training, education, and/or clinical diagnosis of studentsor patients, the user data server 114 may store and analyze each user'sassessments completed or training materials viewed, presentationsattended, courses or tests completed, the user's responses or otherinteractions, assessment or evaluation results, and the like. The userdata server 114 may also include a repository for user-generatedmaterial, such as evaluations and tests completed by users, anddocuments and assignments prepared by users. In the context of mediadistribution and interactive gaming, the user data server 114 may storeand process resource access data for multiple users (e.g., contenttitles accessed, access times, data usage amounts, gaming histories,user devices and device types, etc.). The user data server 114 may alsostore user patterns associated with body movements and/or facialexpressions made during content delivery that may indicate emotions suchas confidence, confusion, frustration, etc.

Administrator server 116 may include hardware and software components toinitiate various administrative functions at the content managementserver 102 and other components within the content distribution network100. For example, the administrator server 116 may monitor device statusand performance for the various servers, data stores, and/or userdevices 106 in the content distribution network 100. When necessary, theadministrator server 116 may add or remove devices from the network 100,and perform device maintenance such as providing software updates to thedevices in the network 100. Various administrative tools on theadministrator server 116 may allow authorized users to set user accesspermissions to various content resources, monitor resource usage byusers and devices 106, and perform analyses and generate reports onspecific network users and/or devices (e.g., resource usage trackingreports, training evaluations, etc.).

Pattern server 118 may include hardware and software components toinitiate various functions related to pattern manipulation as well asinteracting with other components within the content distributionnetwork. The pattern server 118 can compare stored patterns to incomingpatters and linked values to generate pattern values that are used todictate certain workflows performed by the content management server102. The pattern server 118 may also store the incoming patterns andlinked values in appropriate data stores. Pattern server 118 comprises apattern engine and associated functionality.

The content distribution network 100 may include one or morecommunication networks 120. Although only a single network 120 isidentified in FIG. 1, the content distribution network 100 may includeany number of different communication networks between any of thecomputer servers and devices shown in FIG. 1 and/or other devicesdescribed herein. Communication networks 120 may enable communicationbetween the various computing devices, servers, and other components ofthe content distribution network 100. As discussed below, variousimplementations of content distribution networks 100 may employdifferent types of networks 120, for example, computer networks,telecommunications networks, wireless networks, and/or any combinationof these and/or other networks.

The content distribution network 100 may include one or severalnavigation systems or features including, for example, the GlobalPositioning System (“GPS”), GALILEO, or the like, or location systems orfeatures including, for example, one or several transceivers that candetermine location of the one or several components of the contentdistribution network 100 via, for example, triangulation. All of theseare depicted as navigation system 122.

In some embodiments, navigation system 122 can include or severalfeatures that can communicate with one or several components of thecontent distribution network 100 including, for example, with one orseveral of the user devices 106 and/or with one or several of thesupervisor devices 110. In some embodiments, this communication caninclude the transmission of a signal from the navigation system 122which signal is received by one or several components of the contentdistribution network 100 and can be used to determine the location ofthe one or several components of the content distribution network 100.

With reference to FIG. 2, an illustrative distributed computingenvironment 200 is shown including a computer server 202, four clientcomputing devices 206, and other components that may implement certainembodiments and features described herein. In some embodiments, theserver 202 may correspond to the content management server 102 discussedabove in FIG. 1, and the client computing devices 206 may correspond tothe user devices 106. However, the computing environment 200 illustratedin FIG. 2 may correspond to any other combination of devices and serversconfigured to implement a client-server model or other distributedcomputing architecture.

Client devices 206 may be configured to receive and execute clientapplications over one or more networks 220. Such client applications maybe web browser based applications and/or standalone softwareapplications, such as mobile device applications. As an examplediscussed in more detail below, certain client devices 206 havingsufficient authorization credentials may be configured an interactivecontent execution device, which may be configured to initiate, monitor,and control the execution of interactive content resources via otherclient receiver devices 206. Server 202 (e.g., a diagnostic analyzerserver) may be communicatively coupled with the client devices 206 viaone or more communication networks 220. Client devices 206 may receiveclient applications from server 202 or from other application providers(e.g., public or private application stores). Server 202 may beconfigured to run one or more server software applications or services,for example, web-based or cloud-based services, to support contentdistribution and interaction with client devices 206. Users operatingclient devices 206 may in turn utilize one or more client applications(e.g., virtual client applications) to interact with server 202 toutilize the services provided by these components.

Various different subsystems and/or components 204 may be implemented onserver 202. Users operating the client devices 206 may initiate one ormore client applications to use services provided by these subsystemsand components. The subsystems and components within the server 202 andclient devices 206 may be implemented in hardware, firmware, software,or combinations thereof. Various different system configurations arepossible in different distributed computing systems 200 and contentdistribution networks 100. The embodiment shown in FIG. 2 is thus oneexample of a distributed computing system and is not intended to belimiting.

Although exemplary computing environment 200 is shown with four clientcomputing devices 206, any number of client computing devices may besupported. Other devices, such as specialized sensor devices, etc., mayinteract with client devices 206 and/or server 202.

As shown in FIG. 2, various security and integration components 208 maybe used to send and manage communications between the server 202 anduser devices 206 over one or more communication networks 220. Thesecurity and integration components 208 may include separate servers,such as web servers and/or authentication servers, and/or specializednetworking components, such as firewalls, routers, gateways, loadbalancers, and the like. In some cases, the security and integrationcomponents 208 may correspond to a set of dedicated hardware and/orsoftware operating at the same physical location and under the controlof same entities as server 202. For example, components 208 may includeone or more dedicated web servers and network hardware in a datacenteror a cloud infrastructure. In other examples, the security andintegration components 208 may correspond to separate hardware andsoftware components which may be operated at a separate physicallocation and/or by a separate entity.

Security and integration components 208 may implement various securityfeatures for data transmission and storage, such as authenticating usersand restricting access to unknown or unauthorized users. In variousimplementations, security and integration components 208 may provide,for example, a file-based integration scheme or a service-basedintegration scheme for transmitting data between the various devices inthe content distribution network 100. Security and integrationcomponents 208 also may use secure data transmission protocols and/orencryption for data transfers, for example, File Transfer Protocol(FTP), Secure File Transfer Protocol (SFTP), and/or Pretty Good Privacy(PGP) encryption.

In some embodiments, one or more web services may be implemented withinthe security and integration components 208 and/or elsewhere within thecontent distribution network 100. Such web services, includingcross-domain and/or cross-platform web services, may be developed forenterprise use in accordance with various web service standards, such asRESTful web services (i.e., services based on the Representation StateTransfer (REST) architectural style and constraints), and/or webservices designed in accordance with the Web Service Interoperability(WS-I) guidelines. Some web services may use the Secure Sockets Layer(SSL) or Transport Layer Security (TLS) protocol to provide secureconnections between the server 202 and user devices 206. SSL or TLS mayuse HTTP or HTTPS to provide authentication and confidentiality. Inother examples, web services may be implemented using REST over HTTPSwith the OAuth open standard for authentication, or using theWS-Security standard which provides for secure SOAP messages using XMLencryption. In other examples, the security and integration components208 may include specialized hardware for providing secure web services.For example, security and integration components 208 may include securenetwork appliances having built-in features such as hardware-acceleratedSSL and HTTPS, WS-Security, and firewalls. Such specialized hardware maybe installed and configured in front of any web servers, so that anyexternal devices may communicate directly with the specialized hardware.

Communication network(s) 220 may be any type of network familiar tothose skilled in the art that can support data communications using anyof a variety of commercially-available protocols, including withoutlimitation, TCP/IP (transmission control protocol/Internet protocol),SNA (systems network architecture), IPX (Internet packet exchange),Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocols,Hyper Text Transfer Protocol (HTTP) and Secure Hyper Text TransferProtocol (HTTPS), Bluetooth®, Near Field Communication (NFC), and thelike. Merely by way of example, network(s) 220 may be local areanetworks (LAN), such as one based on Ethernet, Token-Ring and/or thelike. Network(s) 220 also may be wide-area networks, such as theInternet. Networks 220 may include telecommunication networks such as apublic switched telephone networks (PSTNs), or virtual networks such asan intranet or an extranet. Infrared and wireless networks (e.g., usingthe Institute of Electrical and Electronics (IEEE) 802.11 protocol suiteor other wireless protocols) also may be included in networks 220.

Computing environment 200 also may include one or more data stores 210and/or back-end servers 212. In certain examples, the data stores 210may correspond to data store server(s) 104 discussed above in FIG. 1,and back-end servers 212 may correspond to the various back-end servers112-116. Data stores 210 and servers 212 may reside in the samedatacenter or may operate at a remote location from server 202. In somecases, one or more data stores 210 may reside on a non-transitorystorage medium within the server 202. Other data stores 210 and back-endservers 212 may be remote from server 202 and configured to communicatewith server 202 via one or more networks 220. In certain embodiments,data stores 210 and back-end servers 212 may reside in a storage-areanetwork (SAN), or may use storage-as-a-service (STaaS) architecturalmodel.

With reference to FIG. 3, an illustrative set of data stores and/or datastore servers is shown, corresponding to the data store servers 104 ofthe content distribution network 100 discussed above in FIG. 1. One ormore individual data stores 301-312 may reside in storage on a singlecomputer server 104 (or a single server farm or cluster) under thecontrol of a single entity, or may reside on separate servers operatedby different entities and/or at remote locations. In some embodiments,data stores 301-311 may be accessed by the content management server 102and/or other devices and servers within the network 100 (e.g., userdevices 106, supervisor devices 110, administrator servers 116, etc.).Access to one or more of the data stores 301-311 may be limited ordenied based on the processes, user credentials, and/or devicesattempting to interact with the data store.

The paragraphs below describe examples of specific data stores that maybe implemented within some embodiments of a content distribution network100. It should be understood that the below descriptions of data stores301-312, including their functionality and types of data stored therein,are illustrative and non-limiting. Data stores server architecture,design, and the execution of specific data stores 301-312 may depend onthe context, size, and functional requirements of a content distributionnetwork 100. For example, in content distribution systems 100 used forclinical diagnosis of examinees such as students and other patients, aswell as distribution systems 100 for professional training andeducational purposes, separate databases or file-based storage systemsmay be implemented in data store server(s) 104 to store student/patientassessment data, trainee data and/or student learning data, clinician,trainer and/or professor data, diagnostic module data (e.g., datadefining which interactive content resources and versions are in whichdiagnostic modules), training module data and content descriptions,training results, evaluation data, and the like. In contrast, in contentdistribution systems 100 used for media distribution from contentproviders to subscribers, separate data stores may be implemented indata stores server(s) 104 to store listings of available content titlesand descriptions, content title usage statistics, subscriber profiles,account data, payment data, network usage statistics, etc.

A user profile data store 301, also referred to herein as a user profiledatabase 301, may include information relating to the end users withinthe content distribution network 100. This information may include usercharacteristics such as the user names, access credentials (e.g., loginsand passwords), user preferences, and information relating to anyprevious user interactions within the content distribution network 100(e.g., requested content, posted content, content modules completed,training scores or evaluations, other associated users, etc.). In someembodiments, this information can relate to one or several individualend users such as, for example, one or several clinicians, patients,students, teachers, administrators, or the like, and in someembodiments, this information can relate to one or several institutionalend users such as, for example, one or several hospitals or clinics,schools, groups of schools such as one or several school districts, oneor several colleges, one or several universities, one or severaltraining providers, or the like. In some embodiments, this informationcan identify one or several user memberships in one or several groupssuch as, for example, a student's membership in a university, school,program, grade, course, class, or the like.

The user profile database 301 can include information relating to auser's status, location, or the like. This information can identify, forexample, a device a user is using, the location of that device, or thelike. In some embodiments, this information can be generated based onany location detection technology including, for example, a navigationsystem 122, or the like.

Information relating to the user's status can identify, for example,logged-in status information that can indicate whether the user ispresently logged-in to the content distribution network 100 and/orwhether the log-in-is active. In some embodiments, the informationrelating to the user's status can identify whether the user is currentlyaccessing content and/or participating in an activity from the contentdistribution network 100.

In some embodiments, information relating to the user's status canidentify, for example, one or several attributes of the user'sinteraction with the content distribution network 100, and/or contentdistributed by the content distribution network 100. This can includedata identifying the user's interactions with the content distributionnetwork 100, the content consumed by the user through the contentdistribution network 100, or the like. In some embodiments, this caninclude data identifying the type of information accessed through thecontent distribution network 100 and/or the type of activity performedby the user via the content distribution network 100, the lapsed timesince the last time the user accessed content and/or participated in anactivity from the content distribution network 100, or the like. In someembodiments, this information can relate to a content program comprisingan aggregate of data, content, and/or activities, and can identify, forexample, progress through the content program, or through the aggregateof data, content, and/or activities forming the content program. In someembodiments, this information can track, for example, the amount of timesince participation in and/or completion of one or several types ofactivities, the amount of time since communication with one or severalsupervisors and/or supervisor devices 110, or the like.

In some embodiments in which the one or several end users areindividuals, and specifically are examinees (e.g., patients or students)of a diagnostic analysis, the user profile database 301 can furtherinclude information relating to these examinees' medical, behavioral,academic and/or educational history. This information can identify oneor several courses of study or assessments that the examinee hasprevious initiated, completed, and/or partially completed, as well asgrades/evaluations received in those courses of study or assessments. Insome embodiments, the examinee's medical, behavioral, academic, and/oreducational history can further include information identifying studentperformance on one or several tests, quizzes, and/or assignments. Insome embodiments, this information can be stored in a tier of memorythat is not the fastest memory in the content delivery network 100.

The user profile database 301 can include information relating to one orseveral examinee/student learning preferences. In some embodiments, forexample, the user, also referred to herein as the examinee (e.g.,patient or student), or the student-user may have one or severalpreferred learning styles and/or assessment styles, one or several mosteffective learning styles, and/or the like. In some embodiments, theexaminee's learning/assessment style can be any style describing how theexaminee best learns or tests, or prefers to learn or test. In oneembodiment, these styles can include, for example, identification of theexaminee as an auditory learner, as a visual learner, and/or as atactile learner. In some embodiments, the data identifying one orseveral examinee learning styles can include data identifying a learningstyle based on the examinee's educational history such as, for example,identifying a student as an auditory learner when the student hasreceived significantly higher grades and/or scores on assessments orassignments and/or in courses favorable to auditory learners. In someembodiments, this information can be stored in a tier of memory that isnot the fastest memory in the content delivery network 100.

In some embodiments, the user profile data store 301 can further includeinformation identifying one or several user skill levels. In someembodiments, these one or several user skill levels can identify a skilllevel determined based on past performance by the user interacting withthe content delivery network 100, and in some embodiments, these one orseveral user skill levels can identify a predicted skill leveldetermined based on past performance by the user interacting with thecontent delivery network 100 and one or several predictive models.

In yet other embodiments the user profile data store 301 can include auser patterns store containing digitally captured facial expressions ofthe user. These patterns can be linked to pattern values that representthe sentiment expressed at the time of capture. For instance, a firstpattern may be associated to a first value where the first valueindicates the user was sad, happy, bored, confident, frustrated, angry,etc. These patterns and values can further be used to evaluate the user,the delivered content, the content provider, the content deliverymethod, etc. For instance, a value that indicates the examinee is boredcould mean that the content in a diagnostic assessment is not beingdelivered at a high enough level, at too high of a level, is notinteresting to the user, etc.

The user profile database 301 can further include information relatingto one or several clinicians, teachers and/or instructors who areresponsible for organizing, presenting, and/or managing the presentationof information to the student. In some embodiments, user profiledatabase 301 can include information identifying courses and/or subjectsthat have been taught by a teacher (or assessments/modules given by aclinician), data identifying courses and/or subjects currently taught bythe teacher, and/or data identifying courses and/or subjects that willbe taught by the teacher. In some embodiments, this can includeinformation relating to one or several teaching styles of one or severalteachers. In some embodiments, the user profile database 301 can furtherinclude information indicating past evaluations and/or evaluationreports received by the clinician/teacher. In some embodiments, the userprofile database 301 can further include information relating toimprovement suggestions received by the clinician/teacher, trainingreceived by the clinician/teacher, continuing education received by theclinician/teacher, and/or the like. In some embodiments, thisinformation can be stored in a tier of memory that is not the fastestmemory in the content delivery network 100.

An accounts data store 302, also referred to herein as an accountsdatabase 302, may generate and store account data for different users invarious roles within the content distribution network 100. For example,accounts may be created in an accounts data store 302 for individual endusers, supervisors, administrator users, and entities such as companiesor educational institutions. Account data may include account types,current account status, account characteristics, and any parameters,limits, restrictions associated with the accounts.

A content library data store 303, also referred to herein as a contentlibrary database 303, may include information describing the individualcontent items (or content resources or data packets) available via thecontent distribution network 100. In some embodiments, these datapackets in the content library database 303 can be linked to form anobject network. In some embodiments, these data packets can be linked inthe object network according to one or several prerequisiterelationships that can, for example, identify the relative hierarchyand/or difficulty of the data objects. In some embodiments, thishierarchy of data objects can be generated by the content distributionnetwork 100 according to user experience with the object network, and insome embodiments, this hierarchy of data objects can be generated basedon one or several existing and/or external hierarchies such as, forexample, a syllabus, a table of contents, or the like. In someembodiments, for example, the object network can correspond to asyllabus such that content for the syllabus is embodied in the objectnetwork.

In some embodiments, the content library data store 303 can comprise asyllabus, a schedule, or the like. In some embodiments, the syllabus orschedule can identify one or several tasks and/or events relevant to theuser. In some embodiments, for example, when the user is a member of agroup such as, a section or a class, these tasks and/or events relevantto the user can identify one or several assignments, quizzes, exams, orthe like.

In some embodiments, the library data store 303 may include metadata,properties, and other characteristics associated with the contentresources stored in the content server 112. Such data may identify oneor more aspects or content attributes of the associated contentresources, for example, subject matter, access level, or skill level ofthe content resources, license attributes of the content resources(e.g., any limitations and/or restrictions on the licensable use and/ordistribution of the content resource), price attributes of the contentresources (e.g., a price and/or price structure for determining apayment amount for use or distribution of the content resource), ratingattributes for the content resources (e.g., data indicating theevaluation or effectiveness of the content resource), and the like. Insome embodiments, the library data store 303 may be configured to allowupdating of content metadata or properties, and to allow the additionand/or removal of information relating to the content resources. Forexample, content relationships may be implemented as graph structures,which may be stored in the library data store 303 or in an additionalstore for use by selection algorithms along with the other metadata.

In some embodiments, the content library data store 303 can containinformation used in evaluating responses received from users. In someembodiments, for example, a user can receive content from the contentdistribution network 100 and can, subsequent to receiving that content,provide a response to the received content. In some embodiments, forexample, the received content can comprise one or several questions,prompts, or the like, and the response to the received content cancomprise an answer to those one or several questions, prompts, or thelike. In some embodiments, information, referred to herein as“comparative data,” from the content library data store 303 can be usedto determine whether the responses are the correct and/or desiredresponses.

In some embodiments, the content library database 303 and/or the userprofile database 301 can comprise an aggregation network also referredto herein as a content network or content aggregation network. Theaggregation network can comprise a plurality of content aggregationsthat can be linked together by, for example: creation by common user(e.g., clinician, patient, student); relation to a common assessment,subject, topic, skill, or the like; creation from a common set of sourcematerial such as a diagnostic module or the like. In some embodiments,the content aggregation can comprise a grouping of content comprisingthe presentation portion of an assessment that can be provided to theuser in the form of, for example, a flash card and an extraction portionthat can comprise the desired response to the presentation portion suchas for example, an answer to a flash card. In some embodiments, one orseveral content aggregations can be generated by the contentdistribution network 100 and can be related to one or several datapackets they can be, for example, organized in object network. In someembodiments, the one or several content aggregations can be each createdfrom content stored in one or several of the data packets.

In some embodiments, the content aggregations located in the contentlibrary database 303 and/or the user profile database 301 can beassociated with a user-creator of those content aggregations. In someembodiments, access to content aggregations can vary based on, forexample, whether a user created the content aggregations. In someembodiments, the content library database 303 and/or the user profiledatabase 301 can comprise a database of content aggregations associatedwith a specific user, and in some embodiments, the content librarydatabase 303 and/or the user profile database 301 can comprise aplurality of databases of content aggregations that are each associatedwith a specific user. In some embodiments, these databases of contentaggregations can include content aggregations created by their specificuser and in some embodiments, these databases of content aggregationscan further include content aggregations selected for inclusion by theirspecific user and/or a supervisor of that specific user. In someembodiments, these content aggregations can be arranged and/or linked ina hierarchical relationship similar to the data packets in the objectnetwork and/or linked to the object network in the object network or thetasks or skills associated with the data packets in the object networkor the syllabus or schedule.

In some embodiments, the content aggregation network, and the contentaggregations forming the content aggregation network can be organizedaccording to the object network and/or the hierarchical relationshipsembodied in the object network. In some embodiments, the contentaggregation network, and/or the content aggregations forming the contentaggregation network can be organized according to one or several tasksidentified in the assessment (or assessment battery), syllabus, scheduleor the like.

A pricing data store 304 may include pricing information and/or pricingstructures for determining payment amounts for providing access to thecontent distribution network 100 and/or the individual content resourceswithin the network 100. In some cases, pricing may be determined basedon a user's access to the content distribution network 100, for example,a time-based subscription fee, or pricing based on network usage and. Inother cases, pricing may be tied to specific content resources. Certaincontent resources (e.g., assessments) may have associated pricinginformation, whereas other pricing determinations may be based on theresources accessed, the profiles and/or accounts of the user, and thedesired level of access (e.g., duration of access, network speed, etc.).Additionally, the pricing data store 304 may include informationrelating to compilation pricing for groups of content resources, such asgroup prices and/or price structures for groupings of resources.

A license data store 305 may include information relating to licensesand/or licensing of the content resources within the contentdistribution network 100. For example, the license data store 305 mayidentify licenses and licensing terms for individual content resourcesand/or compilations of content resources in the content server 112, therights holders for the content resources, and/or common or large-scaleright holder information such as contact information for rights holdersof content not included in the content server 112.

A content access data store 306 may include access rights and securityinformation for the content distribution network 100 and specificcontent resources. For example, the content access data store 306 mayinclude login information (e.g., user identifiers, logins, passwords,etc.) that can be verified during user login attempts to the network100. The content access data store 306 also may be used to storeassigned user roles and/or user levels of access. For example, a user'saccess level may correspond to the sets of content resources and/or theclient or server applications that the user is permitted to access.Certain users may be permitted or denied access to certain applicationsand resources based on their subscription level, training program,course/grade level, etc. Certain users may have supervisory access overone or more end users, allowing the supervisor to access all or portionsof the end user's content, activities, evaluations, etc. Additionally,certain users may have administrative access over some users and/or someapplications in the content management network 100, allowing such usersto add and remove user accounts, modify user access permissions, performmaintenance updates on software and servers, etc.

A source data store 307 may include information relating to the sourceof the content resources available via the content distribution network.For example, a source data store 307 may identify the authors andoriginating devices of content resources, previous pieces of data and/orgroups of data originating from the same authors or originating devices,and the like.

An evaluation data store 308 may include information used to direct theevaluation of users and content resources in the content managementnetwork 100. In some embodiments, the evaluation data store 308 maycontain, for example, the analysis criteria and the analysis guidelinesfor evaluating users (e.g., trainees/students/patients, gaming users,media content consumers, etc.) and/or for evaluating the contentresources in the network 100. The evaluation data store 308 also mayinclude information relating to evaluation processing tasks, forexample, the identification of users and user devices 106 that havereceived certain content resources or accessed certain applications, thestatus of evaluations or evaluation histories for content resources,users, or applications, and the like. Evaluation criteria may be storedin the evaluation data store 308 including data and/or instructions inthe form of one or several electronic rubrics or scoring guides for usein the evaluation of the content, users, or applications. The evaluationdata store 308 also may include past evaluations and/or evaluationanalyses for users, content, and applications, including relativerankings, characterizations, explanations, and the like. Evaluation datastore 308 also includes evaluations tabulated from pattern detection forcontent including instructional content and instructors.

A model data store 309, also referred to herein as a model database 309can store information relating to one or several predictive models. Insome embodiments, these can include one or several evidence models, riskmodels, skill models, or the like. In some embodiments, an evidencemodel can be a mathematically-based statistical model. The evidencemodel can be based on, for example, Item Response Theory (IRT), BayesianNetwork (Bayes net), Logistic Regression, Discriminant FunctionAnalysis, Principal Factor Analysis (PFA), linear and/or non-linearmultiple regression models, multivariate base rate analysis, or thelike. The evidence model can, in some embodiments, be customizable to auser and/or to one or several content items. Specifically, one orseveral inputs relating to the user and/or to one or several contentitems can be inserted into the evidence model. These inputs can include,for example, one or more measures of user skill level, one or moremeasures of content item difficulty and/or skill level, one or moremeasures of symptom severity/behavioral expression, one or more measuresof functional interference, or the like. The customized evidence modelcan then be used to predict the likelihood of the user providing desiredor undesired responses to one or several of the content items.

In some embodiments, the risk models can include one or several modelsthat can be used to calculate one or several model function values. Insome embodiments, these one or several model function values can be usedto calculate a risk probability, which risk probability can characterizethe risk of a user such as a examinee receiving a particularclassification or diagnosis, a student-user failing to achieve a desiredoutcome such as, for example, failing to correctly respond to one orseveral data packets, failure to achieve a desired level of completionof a program, for example in a pre-defined time period, failure toachieve a desired learning outcome, or the like. In some embodiments,the risk probability can identify the risk of the student-user failingto complete 60% of the program.

In some embodiments, these models can include a plurality of modelfunctions including, for example, a first model function, a second modelfunction, a third model function, and a fourth model function. In someembodiments, some or all of the model functions can be associated with aportion of an assessment or program such as, for example, a completionstage and/or completion status of the program. In one embodiment, forexample, the first model function can be associated with a firstcompletion status, the second model function can be associated with asecond completion status, the third model function can be associatedwith a third completion status, and the fourth model function can beassociated with a fourth completion status. In some embodiments, thesecompletion statuses can be selected such that some or all of thesecompletion statuses are less than the desired level of completion of theassessment/program. Specifically, in some embodiments, these completionstatus can be selected to all be at less than 60% completion of theassessment/program, and more specifically, in some embodiments, thefirst completion status can be at 20% completion of theassessment/program, the second completion status can be at 30%completion of the assessment/program, the third completion status can beat 40% completion of the assessment/program, and the fourth completionstatus can be at 50% completion of the assessment/program. Similarly,any desired number of model functions can be associated with any desirednumber of completion statuses.

In some embodiments, a model function can be selected from the pluralityof model functions based on a user's progress through anassessment/program. In some embodiments, the user's progress can becompared to one or several status trigger thresholds, each of whichstatus trigger thresholds can be associated with one or more of themodel functions. If one of the status triggers is triggered by theuser's progress, the corresponding one or several model functions can beselected.

The model functions can comprise a variety of types of models and/orfunctions. In some embodiments, each of the model functions outputs afunction value that can be used in calculating a risk probability. Thisfunction value can be calculated by performing one or severalmathematical operations on one or several values indicative of one orseveral user attributes and/or user parameters, also referred to hereinas program (or assessment) status parameters. In some embodiments, eachof the model functions can use the same program status parameters, andin some embodiments, the model functions can use differentassessment/program status parameters. In some embodiments, the modelfunctions use different assessment/program status parameters when atleast one of the model functions uses at least one program statusparameter that is not used by others of the model functions.

In some embodiments, a skill model can comprise a statistical modelidentifying a predictive skill level of one or several students. In someembodiments, this model can identify a single skill level of a studentand/or a range of possible skill levels of a student. In someembodiments, this statistical model can identify a skill level of astudent-user and an error value or error range associated with thatskill level. In some embodiments, the error value can be associated witha confidence interval determined based on a confidence level. Thus, insome embodiments, as the number of student interactions with the contentdistribution network increases, the confidence level can increase andthe error value can decrease such that the range identified by the errorvalue about the predicted skill level is smaller.

A threshold database 310, also referred to herein as a thresholddatabase, can store one or several threshold values. These one orseveral threshold values can delineate between states or conditions. Inone exemplary embodiments, for example, a threshold value can delineatebetween an acceptable user performance and an unacceptable userperformance, between content appropriate for a user and content that isinappropriate for a user, between risk levels, or the like.

In addition to the illustrative data stores described above, data storeserver(s) 104 (e.g., database servers, file-based storage servers, etc.)may include one or more external data aggregators 311. External dataaggregators 311 may include third-party data sources accessible to thecontent management network 100, but not maintained by the contentmanagement network 100. External data aggregators 311 may include anyelectronic information source relating to the users, content resources,or applications of the content distribution network 100. For example,external data aggregators 311 may be third-party data stores containingdemographic data, education related data, consumer sales data, healthrelated data, and the like. Illustrative external data aggregators 311may include, for example, social networking web servers, public recordsdata stores, learning management systems, educational institutionservers, business servers, consumer sales data stores, medical recorddata stores, etc. Data retrieved from various external data aggregators311 may be used to verify and update user account information, suggestuser content, and perform user and content evaluations.

A pattern store 312 is a database containing patterns with linkedvalues. The patterns represent digitized facial expressions of amultitude of people and the linked values are the sentimentscorresponding to the facial expression at the time the digital patternwas captured. In some cases sentiment values can be averaged over themultitude of patterns with the same sentiment value and or remainsearchable for each pattern and linked value. This is thus a genericpattern database since the patterns and linked values are not associatedwith a particular user of the content distribution network 100, althoughit could be comprised of patterns and values of all or a portion of theusers of the content distribution network 100.

With reference now to FIG. 4, a block diagram is shown illustrating anembodiment of one or more content management servers 102 within acontent distribution network 100. In such an embodiment, contentmanagement server 102 performs internal data gathering and processing ofstreamed content along with external data gathering and processing.Other embodiments could have either all external or all internal datagathering. This embodiment allows reporting timely information thatmight be of interest to the reporting party or other parties. In thisembodiment, the content management server 102 can monitor gatheredinformation from several sources to allow it to make timely businessand/or processing decisions based upon that information. For example,reports of user actions and/or responses, as well as the status and/orresults of one or several processing tasks could be gathered andreported to the content management server 102 from a number of sources.

Internally, the content management server 102 gathers information fromone or more internal components 402-408. The internal components 402-408gather and/or process information relating to such things as: content(e.g., clinical assessments) provided to users; content consumed byusers; responses provided by users (e.g., clinicians/students/patients);user skill levels; content difficulty levels; next content for providingto users; etc. The internal components 402-408 can report the gatheredand/or generated information in real-time, near real-time or alonganother time line. To account for any delay in reporting information, atime stamp or staleness indicator can inform others of how timely theinformation was sampled. The content management server 102 can opt toallow third parties to use internally or externally gathered informationthat is aggregated within the server 102 by subscription to the contentdistribution network 100.

A command and control (CC) interface 338 configures the gathered inputinformation to an output of data streams, also referred to herein ascontent streams. APIs for accepting gathered information and providingdata streams are provided to third parties external to the server 102who want to subscribe to data streams. The server 102 or a third partycan design as yet undefined APIs using the CC interface 338. The server102 can also define authorization and authentication parameters usingthe CC interface 338 such as authentication, authorization, login,and/or data encryption. CC information is passed to the internalcomponents 402-408 and/or other components of the content distributionnetwork 100 through a channel separate from the gathered information ordata stream in this embodiment, but other embodiments could embed CCinformation in these communication channels. The CC information allowsthrottling information reporting frequency, specifying formats forinformation and data streams, deactivation of one or several internalcomponents 402-408 and/or other components of the content distributionnetwork 100, updating authentication and authorization, etc.

The various data streams that are available can be researched andexplored through the CC interface 338. Those data stream selections fora particular subscriber, which can be one or several of the internalcomponents 402-408 and/or other components of the content distributionnetwork 100, are stored in the queue subscription information database322. The server 102 and/or the CC interface 338 then routes selecteddata streams to processing subscribers that have selected delivery of agiven data stream. Additionally, the server 102 also supports historicalqueries of the various data streams that are stored in an historicaldata store 334 as gathered by an archive data agent 336. Through the CCinterface 238 various data streams can be selected for archiving intothe historical data store 334.

Components of the content distribution network 100 outside of the server102 can also gather information that is reported to the server 102 inreal-time, near real-time or along another time line. There is a definedAPI between those components and the server 102. Each type ofinformation or variable collected by server 102 falls within a definedAPI or multiple APIs. In some cases, the CC interface 338 is used todefine additional variables to modify an API that might be of use toprocessing subscribers. The additional variables can be passed to allprocessing subscribes or just a subset. For example, a component of thecontent distribution network 100 outside of the server 102 may report auser response but define an identifier of that user as a privatevariable that would not be passed to processing subscribers lackingaccess to that user and/or authorization to receive that user data.Processing subscribers having access to that user and/or authorizationto receive that user data would receive the subscriber identifier alongwith response reported that component. Encryption and/or uniqueaddressing of data streams or sub-streams can be used to hide theprivate variables within the messaging queues.

The user devices 106 and/or supervisor devices 110 communicate with theserver 102 through security and/or integration hardware 410. Thecommunication with security and/or integration hardware 410 can beencrypted or not. For example, a socket using a TCP connection could beused. In addition to TCP, other transport layer protocols like SCTP andUDP could be used in some embodiments to intake the gatheredinformation. A protocol such as SSL could be used to protect theinformation over the TCP connection. Authentication and authorizationcan be performed to any user devices 106 and/or supervisor deviceinterfacing to the server 102. The security and/or integration hardware410 receives the information from one or several of the user devices 106and/or the supervisor devices 110 by providing the API and anyencryption, authorization, and/or authentication. In some cases, thesecurity and/or integration hardware 410 reformats or rearranges thisreceived information

The messaging bus 412, also referred to herein as a messaging queue or amessaging channel, can receive information from the internal componentsof the server 102 and/or components of the content distribution network100 outside of the server 102 and distribute the gathered information asa data stream to any processing subscribers that have requested the datastream from the messaging queue 412. Specifically, in some embodiments,the messaging bus 412 can receive and output information from at leastone of the packet selection system, the presentation system, theresponse system, and the summary model system. In some embodiments, thisinformation can be output according to a “push” model, and in someembodiments, this information can be output according to a “pull” model.

As indicated in FIG. 4, processing subscribers are indicated by aconnector to the messaging bus 412, the connector having an arrow headpointing away from the messaging bus 412. Only data streams within themessaging queue 412 that a particular processing subscriber hassubscribed to may be read by that processing subscriber if received atall. Gathered information sent to the messaging queue 412 is processedand returned in a data stream in a fraction of a second by the messagingqueue 412. Various multicasting and routing techniques can be used todistribute a data stream from the messaging queue 412 that a number ofprocessing subscribers have requested. Protocols such as Multicast ormultiple Unicast could be used to distributed streams within themessaging queue 412. Additionally, transport layer protocols like TCP,SCTP and UDP could be used in various embodiments.

Through the CC interface 338, an external or internal processingsubscriber can be assigned one or more data streams within the messagingqueue 412. A data stream is a particular type of messages in aparticular category. For example, a data stream can comprise all of thedata reported to the messaging bus 412 by a designated set ofcomponents. One or more processing subscribers may subscribe and receivethe data stream to process the information and make a decision and/orfeed the output from the processing as gathered information fed backinto the messaging queue 412. Through the CC interface 338 a developercan search the available data streams or specify a new data stream andits API. The new data stream might be determined by processing a numberof existing data streams with a processing subscriber.

The CDN 110 has internal processing subscribers 402-408 that processassigned data streams to perform functions within the server 102.Internal processing subscribers 402-408 could perform functions such asproviding content to a user, receiving a response from a user,determining the correctness of the received response, updating one orseveral models based on the correctness of the response, recommendingnew content for providing to one or several users, or the like. Theinternal processing subscribers 402-408 can decide filtering andweighting of records from the data stream. To the extent that decisionsare made based upon analysis of the data stream, each data record istime stamped to reflect when the information was gathered such thatadditional credibility could be given to more recent results, forexample. Other embodiments may filter out records in the data streamthat are from an unreliable source or stale. For example, a particularcontributor of information may prove to have less than optimal gatheredinformation and that could be weighted very low or removed altogether.

Internal processing subscribers 402-408 may additionally process one ormore data streams to provide different information to feed back into themessaging queue 412 to be part of a different data stream. For example,hundreds of user devices 106 could provide responses that are put into adata stream on the messaging queue 412. An internal processingsubscriber 402-408 could receive the data stream and process it todetermine the difficulty of one or several data packets provided to oneor several users, and supply this information back onto the messagingqueue 412 for possible use by other internal and external processingsubscribers.

As mentioned above, the CC interface 338 allows the CDN 110 to queryhistorical messaging queue 412 information. An archive data agent 336listens to the messaging queue 412 to store data streams in a historicaldatabase 334. The historical database 334 may store data streams forvarying amounts of time and may not store all data streams. Differentdata streams may be stored for different amounts of time.

With regards to the components 402-48, the content management server(s)102 may include various server hardware and software components thatmanage the content resources within the content distribution network 100and provide interactive and adaptive content to users on various userdevices 106. For example, content management server(s) 102 may provideinstructions to and receive information from the other devices withinthe content distribution network 100, in order to manage and transmitcontent resources, user data, and server or client applicationsexecuting within the network 100.

A content management server 102 may include a packet selection system402. The packet selection system 402 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., a packetselection server 402), or using designated hardware and softwareresources within a shared content management server 102. In someembodiments, the packet selection system 402 may adjust the selectionand adaptive capabilities of content resources to match the needs anddesires of the users receiving the content. For example, the packetselection system 402 may query various data stores and servers 104 toretrieve user information, such as user preferences and characteristics(e.g., from a user profile data store 301), user access restrictions tocontent recourses (e.g., from a content access data store 306), previoususer results and content evaluations (e.g., from an evaluation datastore 308), and the like. Based on the retrieved information from datastores 104 and other data sources, the packet selection system 402 maymodify content resources for individual users.

In some embodiments, the packet selection system 402 can include arecommendation engine, also referred to herein as an adaptiverecommendation engine. In some embodiments, the recommendation enginecan select one or several pieces of content (e.g., particularinteractive content within clinical assessments and/or diagnosticmodules), also referred to herein as data packets, for providing to auser. These data packets can be selected based on, for example, theinformation retrieved from the database server 104 including, forexample, the user profile database 301, the content library database303, the model database 309, or the like. In some embodiments, these oneor several data packets can be adaptively selected and/or selectedaccording to one or several selection rules. In one embodiment, forexample, the recommendation engine can retrieve information from theuser profile database 301 identifying, for example, a skill level of theuser. The recommendation engine can further retrieve information fromthe content library database 303 identifying, for example, potentialdata packets for providing to the user and the difficulty of those datapackets and/or the skill level associated with those data packets.

The recommendation engine can identify one or several potential datapackets for providing and/or one or several data packets for providingto the user based on, for example, one or several rules, models,predictions, or the like. The recommendation engine can use the skilllevel of the user to generate a prediction of the likelihood of one orseveral users providing a desired response to some or all of thepotential data packets. In some embodiments, the recommendation enginecan pair one or several data packets with selection criteria that may beused to determine which packet should be delivered to a user based onone or several received responses from that user. In some embodiments,one or several data packets can be eliminated from the pool of potentialdata packets if the prediction indicates either too high a likelihood ofa desired response or too low a likelihood of a desired response. Insome embodiments, the recommendation engine can then apply one orseveral selection criteria to the remaining potential data packets toselect a data packet for providing to the user. These one or severalselection criteria can be based on, for example, criteria relating to adesired estimated time for receipt of response to the data packet, oneor several content parameters, one or several assignment parameters, orthe like.

A content management server 102 also may include a summary model system404. The summary model system 404 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., a summarymodel server 404), or using designated hardware and software resourceswithin a shared content management server 102. In some embodiments, thesummary model system 404 may monitor the progress of users throughvarious types of content resources and groups, such as mediacompilations, courses or curriculums in clinical diagnoses, training oreducational contexts, interactive gaming environments, and the like. Forexample, the summary model system 404 may query one or more databasesand/or data store servers 104 to retrieve user data such as associatedcontent compilations (e.g., diagnostic modules), content programs (e.g.,assessments), content completion status, user goals, results, and thelike.

A content management server 102 also may include an response system 406,which can include, in some embodiments, a response processor. Theresponse system 406 may be implemented using dedicated hardware withinthe content distribution network 100 (e.g., a response server 406), orusing designated hardware and software resources within a shared contentmanagement server 102. The response system 406 may be configured toreceive and analyze information from user devices 106. For example,various ratings of content resources submitted by users may be compiledand analyzed, and then stored in a data store (e.g., a content librarydata store 303 and/or evaluation data store 308) associated with thecontent. In some embodiments, the response server 406 may analyze theinformation to determine the effectiveness or appropriateness of contentresources with, for example, a subject matter, an age group, a skilllevel, or the like. In some embodiments, the response system 406 mayprovide updates to the packet selection system 402 or the summary modelsystem 404, with the attributes of one or more content resources orgroups of resources within the network 100. The response system 406 alsomay receive and analyze user evaluation data from user devices106—including patterns associated with facial expressions, supervisordevices 110, and administrator servers 116, etc. For instance, responsesystem 406 may receive, aggregate, and analyze user evaluation data fordifferent types of users (e.g., end users, supervisors, administrators,etc.) in different contexts (e.g., media consumer ratings, trainee orstudent comprehension levels, examinee evaluation scores,clinician/teacher effectiveness levels, gamer skill levels, etc.).

In some embodiments, the response system 406 can be further configuredto receive one or several responses from the user and analyze these oneor several responses. In some embodiments, for example, the responsesystem 406 can be configured to translate the one or several responsesinto one or several observables. As used herein, an observable is acharacterization of a received response. In some embodiments, thetranslation of the one or several response into one or severalobservables can include determining whether the one or several responseare correct responses, also referred to herein as desired responses, orare incorrect responses, also referred to herein as undesired responses.In some embodiments, the translation of the one or several response intoone or several observables can include characterizing the degree towhich one or several response are desired responses and/or undesiredresponses. In some embodiments, one or several values can be generatedby the response system 406 to reflect user performance in responding tothe one or several data packets. In some embodiments, these one orseveral values can comprise one or several scores for one or severalresponses and/or data packets. In other embodiments, the response system406 can be configured to receive one or more patterns from the userdevice 106 corresponding with digitized facial expressions made by theuser of the user device 106 during delivery of the content. The responsesystem 406 can match sentiment values to workflows and initiate theworkflows according to values computed from the patterns received fromuser devices 106.

A content management server 102 also may include a presentation system408. The presentation system 408 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., apresentation server 408), or using designated hardware and softwareresources within a shared content management server 102. Thepresentation system 408 can include a presentation engine that can be,for example, a software module running on the content delivery system.

The presentation system 408, also referred to herein as the presentationmodule or the presentation engine, may receive content resources fromthe packet selection system 402 and/or from the summary model system404, and provide the resources to user devices 106. The presentationsystem 408 may determine the appropriate presentation format for thecontent resources based on the user characteristics and preferences,and/or the device capabilities of user devices 106. If needed, thepresentation system 408 may convert the content resources to theappropriate presentation format and/or compress the content beforetransmission. In some embodiments, the presentation system 408 may alsodetermine the appropriate transmission media and communication protocolsfor transmission of the content resources.

In some embodiments, the presentation system 408 may include specializedsecurity and integration hardware 410, along with corresponding softwarecomponents to implement the appropriate security features contenttransmission and storage, to provide the supported network and clientaccess models, and to support the performance and scalabilityrequirements of the network 100. The security and integration layer 410may include some or all of the security and integration components 208discussed above in FIG. 2, and may control the transmission of contentresources and other data, as well as the receipt of requests and contentinteractions, to and from the user devices 106, supervisor devices 110,administrative servers 116, and other devices in the network 100.

With reference now to FIG. 5, a block diagram of an illustrativecomputer system is shown. The system 500 may correspond to any of thecomputing devices or servers of the content distribution network 100described above, or any other computing devices described herein, andspecifically can include, for example, one or several of the userdevices 106 (e.g., an interactive content receiver device and/or aninteractive content execution device), the supervisor device 110 (e.g.,an interactive content execution device), and/or any of the servers 102,104, 108, 112, 114, 116 (e.g., a diagnostic analyzer server). In thisexample, computer system 500 includes processing units 504 thatcommunicate with a number of peripheral subsystems via a bus subsystem502. These peripheral subsystems include, for example, a storagesubsystem 510, an I/O subsystem 526, and a communications subsystem 532.

Bus subsystem 502 provides a mechanism for letting the variouscomponents and subsystems of computer system 500 communicate with eachother as intended. Although bus subsystem 502 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 502 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Sucharchitectures may include, for example, an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 504, which may be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 500. One or more processors,including single core and/or multicore processors, may be included inprocessing unit 504. As shown in the figure, processing unit 504 may beimplemented as one or more independent processing units 506 and/or 508with single or multicore processors and processor caches included ineach processing unit. In other embodiments, processing unit 504 may alsobe implemented as a quad-core processing unit or larger multicoredesigns (e.g., hexa-core processors, octo-core processors, ten-coreprocessors, or greater.

Processing unit 504 may execute a variety of software processes embodiedin program code, and may maintain multiple concurrently executingprograms or processes. At any given time, some or all of the programcode to be executed can be resident in processor(s) 504 and/or instorage subsystem 510. In some embodiments, computer system 500 mayinclude one or more specialized processors, such as digital signalprocessors (DSPs), outboard processors, graphics processors,application-specific processors, and/or the like.

I/O subsystem 526 may include device controllers 528 for one or moreuser interface input devices and/or user interface output devices 530.User interface input and output devices 530 may be integral with thecomputer system 500 (e.g., integrated audio/video systems, and/ortouchscreen displays), or may be separate peripheral devices which areattachable/detachable from the computer system 500. The I/O subsystem526 may provide one or several outputs to a user by converting one orseveral electrical signals to user perceptible and/or interpretableform, and may receive one or several inputs from the user by generatingone or several electrical signals based on one or several user-causedinteractions with the I/O subsystem such as the depressing of a key orbutton, the moving of a mouse, the interaction with a touchscreen ortrackpad, the interaction of a sound wave with a microphone, or thelike.

Input devices 530 may include a keyboard, pointing devices such as amouse or trackball, a touchpad or touch screen incorporated into adisplay, a scroll wheel, a click wheel, a dial, a button, a switch, akeypad, audio input devices with voice command recognition systems,microphones, and other types of input devices. Input devices 530 mayalso include three dimensional (3D) mice, joysticks or pointing sticks,gamepads and graphic tablets, and audio/visual devices such as speakers,digital cameras or other image sensors, digital camcorders, portablemedia players, webcams, image scanners, fingerprint scanners, barcodereader 3D scanners, 3D printers, laser rangefinders, and eye gazetracking devices. Additional input devices 530 may include, for example,motion sensing and/or gesture recognition devices that enable users tocontrol and interact with an input device through a natural userinterface using gestures and spoken commands, eye gesture recognitiondevices that detect eye activity from users and transform the eyegestures as input into an input device, voice recognition sensingdevices that enable users to interact with voice recognition systemsthrough voice commands, medical imaging input devices, MIDI keyboards,digital musical instruments, and the like.

Output devices 530 may include one or more display subsystems, indicatorlights, or non-visual displays such as audio output devices, etc.Display subsystems may include, for example, cathode ray tube (CRT)displays, flat-panel devices, such as those using a liquid crystaldisplay (LCD) or plasma display, light-emitting diode (LED) displays,projection devices, touch screens, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system500 to a user or other computer. For example, output devices 530 mayinclude, without limitation, a variety of display devices that visuallyconvey text, graphics and audio/video information such as monitors,printers, speakers, headphones, automotive navigation systems, plotters,voice output devices, and modems.

Computer system 500 may comprise one or more storage subsystems 510,comprising hardware and software components used for storing data andprogram instructions, such as system memory 518 and computer-readablestorage media 516. The system memory 518 and/or computer-readablestorage media 516 may store program instructions that are loadable andexecutable on processing units 504, as well as data generated during theexecution of these programs.

Depending on the configuration and type of computer system 500, systemmemory 318 may be stored in volatile memory (such as random accessmemory (RAM) 512) and/or in non-volatile storage drives 514 (such asread-only memory (ROM), flash memory, etc.) The RAM 512 may contain dataand/or program modules that are immediately accessible to and/orpresently being operated and executed by processing units 504. In someimplementations, system memory 518 may include multiple different typesof memory, such as static random access memory (SRAM) or dynamic randomaccess memory (DRAM). In some implementations, a basic input/outputsystem (BIOS), containing the basic routines that help to transferinformation between elements within computer system 500, such as duringstart-up, may typically be stored in the non-volatile storage drives514. By way of example, and not limitation, system memory 518 mayinclude application programs 520, such as client applications, Webbrowsers, mid-tier applications, server applications, etc., program data522, and an operating system 524.

Storage subsystem 510 also may provide one or more tangiblecomputer-readable storage media 516 for storing the basic programmingand data constructs that provide the functionality of some embodiments.Software (programs, code modules, instructions) that when executed by aprocessor provide the functionality described herein may be stored instorage subsystem 510. These software modules or instructions may beexecuted by processing units 504. Storage subsystem 510 may also providea repository for storing data used in accordance with the presentinvention.

Storage subsystem 300 may also include a computer-readable storage mediareader that can further be connected to computer-readable storage media516. Together and, optionally, in combination with system memory 518,computer-readable storage media 516 may comprehensively representremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containing, storing,transmitting, and retrieving computer-readable information.

Computer-readable storage media 516 containing program code, or portionsof program code, may include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or other tangible computer readable media. This can also includenontangible computer-readable media, such as data signals, datatransmissions, or any other medium which can be used to transmit thedesired information and which can be accessed by computer system 500.

By way of example, computer-readable storage media 516 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 516 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 516 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 500.

Communications subsystem 532 may provide a communication interface fromcomputer system 500 and external computing devices via one or morecommunication networks, including local area networks (LANs), wide areanetworks (WANs) (e.g., the Internet), and various wirelesstelecommunications networks. As illustrated in FIG. 5, thecommunications subsystem 532 may include, for example, one or morenetwork interface controllers (NICs) 534, such as Ethernet cards,Asynchronous Transfer Mode NICs, Token Ring NICs, and the like, as wellas one or more wireless communications interfaces 536, such as wirelessnetwork interface controllers (WNICs), wireless network adapters, andthe like. As illustrated in FIG. 5, the communications subsystem 532 mayinclude, for example, one or more location determining features 538 suchas one or several navigation system features and/or receivers, and thelike. Additionally and/or alternatively, the communications subsystem532 may include one or more modems (telephone, satellite, cable, ISDN),synchronous or asynchronous digital subscriber line (DSL) units,FireWire® interfaces, USB® interfaces, and the like. Communicationssubsystem 536 also may include radio frequency (RF) transceivercomponents for accessing wireless voice and/or data networks (e.g.,using cellular telephone technology, advanced data network technology,such as 3G, 4G or EDGE (enhanced data rates for global evolution), WiFi(IEEE 802.11 family standards, or other mobile communicationtechnologies, or any combination thereof), global positioning system(GPS) receiver components, and/or other components.

The various physical components of the communications subsystem 532 maybe detachable components coupled to the computer system 500 via acomputer network, a FireWire® bus, or the like, and/or may be physicallyintegrated onto a motherboard of the computer system 500. Communicationssubsystem 532 also may be implemented in whole or in part by software.

In some embodiments, communications subsystem 532 may also receive inputcommunication in the form of structured and/or unstructured data feeds,event streams, event updates, and the like, on behalf of one or moreusers who may use or access computer system 500. For example,communications subsystem 532 may be configured to receive data feeds inreal-time from users of social networks and/or other communicationservices, web feeds such as Rich Site Summary (RSS) feeds, and/orreal-time updates from one or more third party information sources(e.g., data aggregators 311). Additionally, communications subsystem 532may be configured to receive data in the form of continuous datastreams, which may include event streams of real-time events and/orevent updates (e.g., sensor data applications, financial tickers,network performance measuring tools, clickstream analysis tools,automobile traffic monitoring, interactive assessment monitoring, etc.).Communications subsystem 532 may output such structured and/orunstructured data feeds, event streams, event updates, and the like toone or more data stores 104 that may be in communication with one ormore streaming data source computers coupled to computer system 500.

Due to the ever-changing nature of computers and networks, thedescription of computer system 500 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software, or acombination. Further, connection to other computing devices, such asnetwork input/output devices, may be employed. Based on the disclosureand teachings provided herein, a person of ordinary skill in the artwill appreciate other ways and/or methods to implement the variousembodiments.

With reference now to FIG. 6, a block diagram illustrating oneembodiment of the communication network is shown. Specifically, FIG. 6depicts one hardware configuration in which messages are exchangedbetween a source hub 602 via the communication network 120 that caninclude one or several intermediate hubs 604. In some embodiments, thesource hub 602 can be any one or several components of the contentdistribution network generating and initiating the sending of a message,and the terminal hub 606 can be any one or several components of thecontent distribution network 100 receiving and not re-sending themessage. In some embodiments, for example, the source hub 602 can be oneor several of the user device 106, the supervisor device 110, and/or theserver 102, and the terminal hub 606 can likewise be one or several ofthe user device 106, the supervisor device 110, and/or the server 102.In some embodiments, the intermediate hubs 604 can include any computingdevice that receives the message and resends the message to a next node.

As seen in FIG. 6, in some embodiments, each of the hubs 602, 604, 606can be communicatively connected with the data store 104. In such anembodiments, some or all of the hubs 602, 604, 606 can send informationto the data store 104 identifying a received message and/or any sent orresent message. This information can, in some embodiments, be used todetermine the completeness of any sent and/or received messages and/orto verify the accuracy and completeness of any message received by theterminal hub 606.

In some embodiments, the communication network 120 can be formed by theintermediate hubs 604. In some embodiments, the communication network120 can comprise a single intermediate hub 604, and in some embodiments,the communication network 120 can comprise a plurality of intermediatehubs. In one embodiment, for example, and as depicted in FIG. 6, thecommunication network 120 includes a first intermediate hub 604-A and asecond intermediate hub 604-B.

With reference now to FIG. 7, a block diagram is shown illustrating anexample of a diagnostic and content selection system 700 for interactivecontent resources. As shown in this example, a diagnostic and contentselection system 700 may include an interactive content execution device(or execution device) 710, one or more interactive content receiverdevices (or receiver devices) 740, one or more (or execution clientdevices) 710, and a diagnostic analyzer server 720. As discussed below,diagnostic analyzer server 720 may be configured to analyze executiondata from the execution of interactive content resources on variousreceiver client devices 740, which may be received via the intermediaryexecution device 710, to determine potential diagnoses, probabilities ofdiagnoses, and then to select subsequent interactive content resourcesfor execution on particular receiver devices 740 via the executiondevice 710. Similarly, the execution device 710 and receiver devices 740may operate in conjunction with one another, and/or with the diagnosticanalyzer server 720 and content resource data stores 730 to receive andexecute interactive content resources for users operating the receiverdevices 740. As discussed below, the exeuction of content resources maybe initiated by a first user (e.g., clinician, teacher, doctor, or otheradministrator) operating the execution device 710, but may cause theinteractive content (e.g., assessment items such as questions and otherinteractive media) to be output to a second user operating a receiverdevice 740. Additionally, during execution of the content resources, thesecond users operating the receiver devices 740 may interact with thecontent by providing response (e.g., answers to questions) and/or anyother feedback, which is transmitted back to the execution device 710.Such feedback may be provided and transmitted during or after executionof the content resources, and the execution device 710 may monitor andcontrol the execution of the content resources that are provided toreceiver devices 740. Thus, for example, a clinician operating anexecution device 710 may select which interactive content from anassessment or diagnostic module will be transmitted and rendered on thereceiver device 740, also may choose to execute interactive contentresources non-linearly (e.g., skipping between questions or subtests,restarting, etc.), and may choose to terminate early an execution of acontent resource. As discussed above, various types of interactivecontent resources may be analyzed, selected, and executed within thediagnostic and content selection system 700 and other related systemsdescribed herein. For example, the interactive content resources mayinclude diagnostic software modules having one or more clinicalassessments organized into tests and subtests. Additional examples ofinteractive content resources may include audio/video media resources,gaming software resources, eCommerce related resources, professionaltraining and educational resources, assessments, etc.

As noted above, in some embodiments the content resources transmitted toand executed by content execution devices 710 and content receiverdevices 740, as well as the content resources analyzed and selected bythe diagnostic analyzer server 720 for particular contentreceivers/executors may be diagnostic modules corresponding toassessment resources (or assessments). As used herein, diagnosticmodules (or assessments) may refer to interactive software and/or mediapackages configured for execution on one or more client devices 710and/or 740. In various examples, assessments may be used to performmedical, behavioral and/or educational evaluations of particularindividuals. For instance, an assessment may be requested and receivedat an execution client device 710 by an authorized content executor,such as a specialized clinician or educator. Specific assessments may bedesigned to be administered only by specifically qualified contentexecutors, such as school psychologists, special needs educators,clinical psychologists, speech pathologists, and the like. Specificassessments also may be designed to be administered to specificrecipients or end users, such as particular patients or students withina predetermined age range or developmental level, or particular endusers having a predetermined diagnosed condition, etc. As illustrated inFIG. 7, the recipients or end users may operate the receiver devices 740during execution of the assessments, during which the recipients mayreceive and respond to the interactive assessment content transmittedfrom the execution device 710 at the direction of the content executor.For instance, an authorized clinician may initiate execution of anassessment on an execution device 710 which is connected to a receiverdevice 740 (e.g., paired-with via Bluetooth or other short-rangewireless protocol) operated by the particular patient or student beingevaluated. During the execution, the clinician may monitor and controlthe navigation of the assessment, controlling what content istransmitted to and output on the receiver device 740, and reviewing inreal-time the user's responses to the content.

As data objects, diagnostic modules or assessments may correspond toindividual and standalone content resources, or may correspond to groupsof related resources. For instance, an assessment may refer to a groupof related interactive content resources, each of which is a componentor subtest of the assessment. In some cases, an assessment consisting ofmultiple components may be provided to execution devices 710 and/orreceiver devices 740 as a single content resource, while in other casesclient devices 710 and 740 may request/receive the individual contentcomponents of an assessment as separate content resources rather thanrequesting/receiving the entire assessment. Additionally, particularcontent executors (e.g., clinicians, therapists, etc.) may have licensesand/or subscriptions for individual assessments (each of which havingone or more components) and/or for groups or packages of relatedassessments. As discussed below, the selections and recommendations ofparticular assessments (and/or particular components within assessments)provided to content executors may be based in part on whether theselected assessments are covered by the existing licenses orsubscriptions of the content executor.

Further, assessments and/or individual content components withinassessments may have various execution restrictions and requirementsbased on the execution device 710 or the content executor operating theexecution device 710. Similarly, execution restrictions and requirementsalso may be based on the particular receiver device 740 and/or the enduser recipient operating the receiver device 740 to receive and torespond to the assessment. For examples, certain assessments may haveparticular hardware and/or network requirements, and thus may beprevented from being selected for execution by a content executor ordownloaded or executed by an execution device 710 not meeting thosehardware and/or network requirements. Similarly, if the particularreceiver device 740 does not satisfy the hardware requirements (e.g.,minimum processor or memory capacity, I/O or graphics capabilities suchas sound, touch screen, movement sensors, GPS, 3D capabilities, etc.),then the execution of the assessment on the execution device 710 may beprohibited when the execution device 710 is connected to the particularreceiver device 740. Similarly, for network requirements, the diagnosticanalyzer server 720 and/or the execution device 710 may prevent anassessment from being selected for and/or transmitted to the executiondevice 710, or from being executed on the execution device 710, if andwhen the current network conditions (e.g., current bandwidth, congestionlevels, etc.) of the connection between the execution device 710 andreceiver device 740 do not satisfy the network requirements, and/or whenthe current connection of the execution client device 710 to its accessnetwork (e.g., an LTE network or a 2G, 3G, or 5G, etc. wireless network)does not satisfy the network requirements associated with theassessment. Further, in some embodiments, neither an Internet connectionnor any access network need be available to perform the execution,evaluation, and selection of test content. For example, as discussedbelow, the analyzer server 720 may be optional in some embodiments,where all analyses and selection processes/algorithms are performed bythe execution device 710 or directing by the receiver device 740.Additionally, the communication between the execution device 710 and theone or more receiver devices 740 need not require any network, but maybe performed entirely via Bluetooth or other short-range connection.

Additional requirements associated with an assessment may relate to theindividual content executor. For example, the diagnostic analyzer server720 may prevent a particular assessment from being selected for,transmitted to, or executed by an execution device 710 unless thecontent executor (e.g., clinician, medial professional, educator, etc.)initiating and controlling the assessment has a required authorizationlevel. Such authorization levels may correspond to the accesspermissions of a particular content executor (e.g., determined based ontheir login credentials) on any of the servers or devices in the system700. The authorization level of the content executor also may correspondto the professional qualifications of the content executor, such asprofessional credentials, degrees, certifications, and/or licenses ofthe particular clinician, educator, etc. Thus, certain assessments maybe authorized to be selected for, transmitted to, and/or executed onexecution devices 710 by content executors having certain accesspermissions within the system 700 and/or having certain professionalqualifications, while the same assessments may be restricted from othercontent executors not having the required access permissions and/orprofessional qualifications. Other types of assessment restrictions maybe based on characteristics of the end user (e.g., patient or student)operating the receiver device 740. For example, the diagnostic analyzerserver 720 and/or the execution device 710 may enforce restrictions thatensure that the examinee taking the assessment, such as a patient orstudent must be within a predetermined age range or developmental level,or must have a particular predetermined diagnosed condition, etc.

Additionally or alternatively, the assessment requirements for contentexecutors may be based on the organization of the content executor. Thatis, certain assessments may be authorized to be selected for,transmitted to, and/or executed by content executors associated withcertain organizations but not others. Additional assessment requirementsfor content executors may be location based, so that only contentexecutors currently within a particular geographic jurisdiction (e.g.,one or more specific countries, states, counties, etc.), or atparticular locations (e.g., specific hospitals, schools, medicaloffices, etc.), may be authorized for certain assessments. Similarlocation-based assessment requirements may be enforced based on thelocation of the receiver device 740 in addition to, or instead of, thelocation of the execution device (e.g., in situations when the contentexecutor and content recipient are not at the same physical location).Further examples of the assessment requirements may include limitationson the number and/or frequency of executions of an assessment by aparticular content executor or to a particular content recipient, andrestrictions on the times and days that an assessment may be executed bya particular content executor and/or to a particular content recipient.

Any of the above requirements associated with an assessment (and/or withindividual components within assessments) may be implemented within andenforced by the diagnostic and content selection system 700 usingvarious techniques. As discussed below system 700 may be animplementation of a content distribution network 100 in which executiondevices 710 are able to request, receive, and execute interactivecontent resources from one or more content data stores 730, eitherdirectly or through the diagnostic analyzer server 720). Thus, any ofthe various types of assessment restrictions discussed above may beenforced by execution devices 710 and/or content data stores 730whenever a content executor attempts to download and/or initiateexecution of an assessment on an execution device 710. Additionally, asdiscussed below, the diagnostic analyzer server 720 may be configured toanalyze assessment results and additional data, in order to select andrecommend subsequent assessments of interactive content resources forparticular content receipients based on the analysis. Therefore, any ofthe various types of assessment restrictions discussed above may beenforced by the diagnostic analyzer server 720, by selecting (or notselecting) interactive content resources for a particular contentexecutor based on determinations that the content executor, theexecution device 710, the content recipient, or the receiver device 740is (or is not) authorized for the interactive content resources. In suchembodiments, the diagnostic analyzer server 720 may filter theinteractive content resources selected for and/or output to theexecution device 710, to include only those interactive contentresources that are authorized for execution by the content executor(and/or the executor's organization), on the execution device 710, tothe content receipient, on the recipient device 740, and at the currenttime, current execution location, etc.

In some embodiments, the diagnostic and content selection system 700 maybe integrated within, or configured to operate in collaboration with,one or more content distribution networks 100. For example, system 700may be the same as, or may operate within or in collaboration with, anyof the content distribution network (CDNs) 100 described above. Thus,specific examples of diagnostic and content selection systems 700 mayinclude, without limitation, educational and professional trainingsystems and networks, interactive gaming systems and networks,clinical/educational assessment distribution systems and networks, andenterprise application systems and networks, websites and otherInternet-based systems and networks. Accordingly, in the variousdifferent diagnostic and content selection systems 700, contentresources may correspond to assessments and/or assessment components orpackages (e.g., diagnostic modules), while in other systems 700 theresources may correspond to educational/training resources (e.g., ineducational and professional training CDNs 100), evaluation or surveyresources (e.g., in enterprise applications or online Internet-basedCDNs 100), or product/media resources (e.g., in interactive gaming ormedia distribution CDNs 100), etc.

In some cases, the diagnostic analyzer server 720 may be implementedwithin one or more content management servers 102 and/or other CDNservers, the content resource data store(s) 730 may correspond to one ormore content servers 112 and/or data store servers 104, and both thecontent execution devices 710 and/or the content receiver devices 740may correspond to the user devices 106 and 110 described above inreference to CDN 100. Thus, within the diagnostic and content selectionsystem 700 (which may also be referred to as CDN 700 when describingcertain embodiments), execution devices 710 and/or receiver devices 740may interact with the diagnostic analyzer server 720 to upload contentexecution data and receive selections and/or recommendations ofadditional content resources (e.g., assessments) to be executed. Asdiscussed below, the diagnostic analyzer server 720 may maintain executevarious algorithms and subsystems, and may use variousresponse-diagnosis analytics data stores 725 storing content resourceexecution data and/or association data between content resources andpotential diagnoses. Additionally, execution devices 710 may interactwith content resource data stores 730, directly or indirectly, torequest/receive particular interactive content resources (e.g.,assessments) based on the selections determined and provided by thediagnostic analyzer server 720. Although the diagnostic analyzer server720, analytics data store 725, and content resource data store 730 areshown as separate components in this example, in other embodiments theymay be implemented within the same servers and/or same data centers. Inother examples, a diagnostic analyzer server 720 may be implementedusing one or more computer servers, and other specialized hardware andsoftware components, separately from any other CDN components such ascontent servers 112, content management servers 102, data store servers104, and the like. In these examples, the diagnostic analyzer server 720may be configured to communicate directly with execution devices 710and/or directly with receiver devices 740, or may communicate indirectlythrough content management servers 102 and/or other components andcommunications networks of the CDN 700.

In order to perform these features and other functionality describedherein, each of the components and sub-components discussed in theexample diagnostic and content selection system 700 for interactivecontent resources may correspond to a single computer server or acomplex computing system including a combination of computing devices,storage devices, network components, etc. Each of these components andtheir respective subcomponents may be implemented in hardware, software,or a combination thereof. Certain execution devices 710 may communicatedirectly with the diagnostic analyzer server 720, while other executiondevices 710 may communicate with the diagnostic analyzer server 720indirectly via one or more intermediary network components (e.g.,routers, gateways, firewalls, etc.) or other devices (e.g., contentmanagement servers 102, content servers 112, etc.). Although thephysical network components have not been shown in this example so asnot to obscure the other elements depicted in the figure, it should beunderstood that any of the network hardware components and networkarchitecture designs may be implemented in various embodiments tosupport communication between the servers and devices in the system 700.Additionally, different execution devices 710 may use different networksand networks types to communicate with the diagnostic analyzer server720 and/or the receiver device 740, including one or moretelecommunications networks, cable networks, satellite networks,cellular networks and other wireless networks, and computer-based IPnetworks, and the like. Further, certain components within system 700may include special purpose hardware devices and/or special purposesoftware, such as those included in I/O subsystems 711 and 741, andclient application memory 714 and 744 of the execution devices 710 andreceiver device 740, as well as those within the API 721 and processingengines within the memory 724 of the diagnostic analyzer server 720,discussed below.

Although the functionality of system 700 may be described below in termsof a client-server model, it should be understood that other computingenvironments and various combinations of servers and devices may be usedto perform the functionality described herein in other examples. Forinstance, certain analyses of content resource executions may beperformed on execution devices 710 and/or receiver devices 740, whiledeterminations of correlations between assessment performance anddiagnoses, and the selections of additional content resources forparticular content recipients may be performed by a web-based server(e.g., diagnostic analyzer server 720) in collaboration with a clientapplication (e.g., web browser or standalone client application)executing on execution devices 710. In other cases these techniques maybe performed entirely by a specialized diagnostic analyzer server 720,or entirely by software executing on an execution device 710. In otherexamples, a client-server model may be used as shown in system 700, butdifferent functional components and processing tasks may be allocated tothe client-side or the sever-side in different embodiments.Additionally, the content resource data store 730 and analytics datastore 725 may be implemented as separate servers or storage systems insome cases, and may use independent hardware and software servicecomponents.

Interactive content execution devices 710 and/or receiver devices 740may include desktop or laptop computers, smartphones, tablet computers,and other various types of computing devices, each of which may includesome or all of the hardware, software, and networking componentsdiscussed above. Specifically, an execution device 710 and/or receiverdevice 740 may be any computing device with sufficient processingcomponents, memory and software components, and I/O system componentsfor interacting with users (e.g., content executors and contentrecipients), and supporting communication with the diagnostic analyzerserver 720 and content resource data stores 730 to select and receiveassessments (or other resources) for execution. Accordingly, executiondevices 710 and receiver devices 740 may include the necessary hardwareand software components to establish the network connections with eachother (e.g., during an execution/assessment session) and with the otherdevices/servers in the system 700, as well as the security andauthentication capabilities, and capabilities for assessment resourcestorage, validation, execution, and responses/feedback. In this example,execution devices 710 and receiver devices 740 may each include an I/Osubsystem 711 and 741 for interacting with their respective users (e.g.,content executors and content recipients). Devices 710 and 740 also mayhave network interface controllers 712 and 742, processing units 713 and743, memory 714 and 744 configured to operate their respective clientsoftware applications. Execution devices 710 and receiver devices 740both may be configured to receive and execute various programmatic andgraphical interfaces to receive, store, and execute assessment resourceshaving various types of assessment components and functionality.Accordingly, each I/O subsystem 711 and 741 may include hardware andsoftware components to support a specific set of output capabilities(e.g., LCD display screen characteristics, screen size, color display,video driver, speakers, audio driver, graphics processor and drivers,etc.), and a specific set of input capabilities (e.g., keyboard, mouse,touchscreen, voice control, cameras, facial recognition, gesturerecognition, etc.). Different execution devices 710 and/or receiverdevices 740 may support different input and output capabilities withintheir I/O subsystems 711 and 741, and thus different types ofinteractions with assessments/components may be compatible orincompatible with certain devices 710 or 740. For example, certaininteractive assessments (or other types of content resources) mayrequire specific types of processors, graphics components, networkcomponents, or I/O components in order to be optimally executed via anexecution device 710 and/or output via a receiver device 740. In someembodiments, users may establish user-specific preferences for executingspecific types of assessments or other resources on specific types ofdevices 710 and 740. Additionally, as shown in this example, the memory714 and 744 of devices 710 and 740 may include web browser softwarehaving browser-native support for JavaScript Object Notation (JSON). Insome embodiments, JSON data objects may be generated and stored withinthe browser memory of execution devices 710 and/or receiver devices 740,and may be used to implement the user interactions and feedback logicfor assessments and/or other types of interactive content resources.

In some embodiments, the diagnostic analyzer server 720 may generate andprovide the software interfaces (e.g., via API 721, a web-basedapplication or other programmatic or graphical interface techniques)used by the execution devices 710 to request/receive content resources,and to provide selections of additional content resources to theexecution devices 710. For example, in response to receiving andvalidating login credentials from an execution device 710, or atpredetermined times before logins are received, the diagnostic analyzerserver 720 may access the response-diagnoses correlations and analyticsdata stores 725 to retrieve and analyze assessment execution data, anddetermine an initial battery of assessments for the content executoroperating the execution device 710. Then, after receiving and analyzingthe results of the initial battery of assessments, the diagnosticanalyzer server 720 may select additional assessments based on contentrecipient data, the results of the initial assessments, and/oradditional data. In other to perform the tasks described herein,diagnostic analyzer server 720 and/or data stores 725-730 may includecomponents such as network interface controllers 722, processing units723, and memory 724 configured to store server software, handleauthentication and security, and store/retrieve assessments and othercontent resources from data stores 730, etc. The diagnostic analyzerserver 720 and data stores 725-730 may be implemented as separatesoftware (and/or storage) components within a single computer server insome examples, while in other examples may be implemented as separatecomputer servers/systems having separate dedicated processing units,storage devices, and/or network components.

Referring now to FIG. 8, a swim lane diagram is shown illustratingcertain techniques for retrieving and executing content resources usingone or more interactive content receiver devices 740 connected to aninteractive content execution device 710 and/or a diagnostic analyzerserver 720. The steps in this example, labeled as numbers (1-8) maycorrespond to steps performed by a receiver device, execution device710, and/or diagnostic analyzer server 720, and more specifically mayrepresent transmissions that may occur between devices during theexecution and evaluation of interactive content resources, and thediagnostic analyses and subsequent selection of additional contentresources based on the analyses.

Step 1 represents the establishment of a connection between the contentexecution device 710 and one or more receiver devices 740. As notedabove, in some cases, the content executor operating the executiondevice 710 may be at the same physical location as the content recipientoperating a receiver device 740. In such cases, the connection in step 1may be implemented using a short-range wireless connection, for example,pairing the devices 710 and 740 using Bluetooth. Additionally, the useof encryption and/or other secure protocols might not be required insome cases when the execution device 710 and receiver device 740 are inthe same physical location, and using short-range wireless transmissionsto communicate, even when transmitted secure and confidential data,since the risk of the data being intercepted or comprised may beminimal. In step 2, a connection is established between the contentexecution device 710 and the diagnostic analyzer server 720. In contrastto step 1, the connection in step 2 may be established over multiplepublic and/or unsecure networks (e.g., the Internet, local accessnetworks, and other IP or packet-switching networks, etc.). Thus,communication sessions between the execution device 710 and thediagnostic analyzer server 720 may be established in step 2 usingvarious secure protocols and/or encryption techniques to protect theconfidential data that may be transmitted, such as confidential patientor student records, medical history data, assessment results, financialor demographic information of users, etc. Additionally, the connectionof step 2 may be established before the connection of step 1 in somecases.

In step 3, to commence a resource execution session (e.g., a clinicaldiagnostic session) with the content recipient operating the receiverdevice 740, the context executor may first use the execution device 710to request a set of initial diagnostic modules from the diagnosticanalyzer server 720. As noted above, the diagnostic modules may includeone or more interactive software resources corresponding to one or moreassessments, including individual assessment components, assessments ortests/subtests, or batteries of related assessments. In step 4, thediagnostic analyzer server 720 may receive and respond to the request bytransmitting the requested diagnostic modules to the execution device710. Although in this example, the execution device 710 may request andreceive resources at the direction of a content executor (e.g.,clinician, teacher, administrator, etc.), in other examples the initialset of diagnostic modules may be selected and provided automatically bythe execution device 710 and/or the diagnostic analyzer server 720,based on additional sources of data related to the content recipient.Additionally, although this example shows the execution device 710receiving diagnostic modules from the diagnostic analyzer server 720, inother implementations the execution device 710 may retrieve diagnosticmodules directly from a content resource data store 730. In certaincases, the diagnostic analyzer server 720 might provide a specificlocation (e.g., URL) or authorization code allowing the execution device710 to retrieve the particular diagnostic modules from the contentresource data store 730, while in other cases the execution device 710may be authorized to retrieve any and all desired diagnostic modulesdirectly from the content resource data store 730 without needing anyassistance or authorization from the diagnostic analyzer server 720.

In step 5, after execution device 710 receives the initial set ofdiagnostic modules, the context executor may initiate the execution ofthe diagnostic modules on the execution device 710. Based on theBluetooth pairing or other network connection established between theexecution device 710 and the receiver device 740, along with thecorresponding communications between the active client applicationsrunning on the execution device 710 and the receiver device 740, theexecution of the diagnostic modules on the execution device 710 maycause the individual interactive assessment components (e.g., clinicalassessment questions or other diagnostic/assessment content) to betransmitted to and rendered on the receiver device 740. In step 6, theresponses and/or feedback from the content recipient operating thereceiver device 740 may be collected by the receiver device 740 andtransmitted back to the execution device 710 via the connection.

In step 7, the execution device 710 may measure the recipientperformance on the initial set of diagnostic modules based on theresponses/feedback data received in step 6, and may transmit theperformance measurement data to the diagnostic analyzer server 720 foranalysis. In some cases, performance measurements may correspond toscores or evaluations of assessment content resources, which may beperformed automatically by the software of the execution device 710and/or by the individual content executor. In some embodiments,additional context data collected from the receiver device 740 and/orthe execution device 710 relating to the content recipient, such as userresponse times to assessment content, facial expressions, behavioral orbody language analysis data, and/or subjective observations of thecontext executor may be incorporated into the performance measurementsof step 7. The execution device 710 may then transmit the performancemeasurements for the content recipient, as well as optional additionaldata related to the execution of the diagnostic modules (e.g., dataidentifying the content executor, specifications of the execution device710 and/or receiver device 740, execution time or location,environmental conditions associated with the assessment such asmovement, background noise, temperature, etc.), to the diagnosticanalyzer server 720 for analysis.

In step 8, the diagnostic analyzer server 720 may generate selections ofadditional assessments/diagnostic modules to be executed within thediagnostic analysis of the content recipient, and may transmit theassessment/diagnostic module selections to the execution device 710. Asdiscussed below in more detail in reference to FIG. 10, the selectionsof additional assessments/diagnostic modules for the content recipientmay be determined based on various statistical data, analysistechniques, and algorithms, based on the performance measurement datareceived in step 7 as well as any additional data relating to theexecution session received from the execution data and/or data relatingto the content recipient received from additional data sources. Asdiscussed below, the analyses performed in the diagnostic analyzerserver 720 may include determining one or more potential diagnosesassociated with the content recipient, as well as probabilitiesassociated with each of the potential diagnoses. Additional diagnosticmodules may be selected in response to determining that the assessmentcontent within those diagnostic modules is likely to affect theprobabilities (in either the positive or negative direction) of one ormore of the potential diagnoses of the content recipient, therebyimproving the efficiency and accuracy of the diagnostic analysisprocess.

In this example, step 8 may include transmitting only a listing (e.g.,names and descriptions) of the selected diagnostic modules to theexecution device 710 for display, so that in step 9 the context executormay request/retrieve one or more of the selected diagnostic modules tobe used in the diagnostic assessment, and in step 10 the diagnosticanalyzer server 720 may transmit only the particular diagnostic modulesselected by the content executor to the execution device. However, inother examples, the diagnostic analyzer server 720 may directly initiatetransmission of the diagnostic modules in step 8 (rather than onlymodule data/listings), and thus steps 9-10 need not be performed in suchcases.

After the completion of these steps, the content executor may initiateexecution of any or all of the additional diagnostic modules receivedfrom the diagnostic analyzer server 720 (thus returning to step 5), tocontinue the ongoing diagnostic assessment of the content recipientoperating the recipient device 740. Further, these steps may beperformed in real-time or near real-time, allowing the diagnosticassessment to continue uninterrupted and without significant delay inperforming evaluations/scoring, or for updating analyses of diagnosticdeterminations and probabilities used to select the additionalassessments/modules. Additionally, the steps in this example may besimilarly performed using different levels of assessment granularity andfrequency for evaluating and updating the analysis. For example, incertain embodiments, the data transmitted in steps 5-10 may correspondto diagnostic modules containing entire assessments (e.g., tests orsubtests). In other embodiments, each user response or feedback data,such as a response to a single question within an assessment, or anysingle item of user feedback data detected by the receiver device 740,may be transmitted back to the execution device 710 and may triggerupdated analyses and an updated performing of steps 7-10, even while theexecution of the same assessment continues on the execution device 710and receiver device 740. The granularity of such embodiments may furtherimprove the efficiency of the diagnostic analysis, and may be performedseamlessly via background processes which are transparent to the contentrecipient and/or the content executor.

Referring now to FIG. 9, a flow diagram is shown illustrating a processof executing, monitoring, and evaluating diagnostic analysis of contentrecipients using content execution devices. As described below, thesteps in this process may be performed by one or more components in thediagnostic and content selection system 700 described above. Forexample, each of the steps 901-908 may be performed by a contentexecution device 710 in communication with a diagnostic analyzer server720 and/or one or more receiver devices 740. However, in other examples,one or more of steps 901-908 may be performed by or in conjunction witha diagnostic analyzer server 720 or receiver devices 740, using one ormore data stores 725-730. It should also be understood that the variousfeatures and processes described herein, including executing diagnosticmodules and transmitting assessment content to receiver devices 740, aswell as receiving and evaluating the response/feedback data associatedwith the execution of the diagnostic modules, need not be limited to thespecific systems and hardware implementations described above in FIGS.1-7.

In step 901, an execution device 710 may receive a set of initialdiagnostic modules that may be executed for a content recipient on oneor more of the content receiver devices 740 associated with theexecution device 710. The initial set of diagnostic modules may beprovided automatically, for example, by the diagnostic analyzer server720 in response to the establishment of a connection and the initiationof a clinical diagnosis session. In step 902, a content executoroperating the execution device 710 (e.g., a clinician, teacher,administrator, etc.) may select one or more of the diagnostic modules tobe executed during the clinical diagnosis session. For example,referring briefly to FIG. 11, an example user interface is shown inwhich a content executor operating an execution device 710 has beenpresented with an initial set of diagnostic modules within an“Instrument Library” 1101. In this example, the content executor maydrag selections of diagnostic modules from the library 1101 to the“Assessment Battery” 1102 to be executed during the clinical diagnosissession. After the content executor finalizes the initial set ofdiagnostic modules to be executed, the content executor may commence theexecution session (e.g., via the “Next” button), at which time theselected diagnostic modules (or assessments) in the assessment battery1102 may be retrieved by the execution device 710, from the diagnosticanalyzer server 720 and/or directly from a content resource library 730.

As shown in this example, the content executor may be provided thefunctionality to select, via drag-and-drop or other techniques, whichinitial set of diagnostic modules are to be executed. However, in otherexamples, initial set of diagnostic modules may be determinedautomatically, by the execution device 710 and/or the diagnosticanalyzer server 720. These automatically determined initial sets ofdiagnostic modules may be fixed and uneditable by the user in somecases, thus rendering steps 901 and 902 unnecessary, while in othercases, automatically determined initial sets of diagnostic modules maybe editable by the user. In either case, the initial diagnostic modulesmay correspond to a standard battery of assessments to be provided tothe content recipient end user (e.g., patient, student, etc.) as astarting point to commerce the clinical diagnosis.

In some embodiments, the initial diagnostic modules selected by theexecution device 710 and/or the diagnostic analyzer server 720 may bebased on jurisdictional requirements for clinical diagnosis, so that themodules/assessments may be selected based on the current location orjurisdiction (e.g., country, state, city, county, school or schooldistrict, etc.) of the content execution device 710 and/or the contentreceiver device 740. Such jurisdictional requirements may be enforced bythe diagnostic analyzer server 720 and/or the execution device 710,which may use device location data (e.g., GPS data, LAN or accessnetwork data, etc.) to determine the location/jurisdiction in which theclinical assessment is being performed, and then providing theappropriate diagnostic modules. Additionally or alternatively, thestandard set of initial assessments may be based on characteristics ofthe content recipient, such as the age, medical history, previousdiagnoses, current academic performance levels, etc. For instance, basedon an age of a student content recipient to be diagnosed, and a note orchecklist from the student's teacher or parent identifying specificlearning or behavior concerns, the diagnostic analyzer server 720 mightautomatically select an initial set of diagnostic modules to be providedto the execution device 710. In still other examples, the initial set ofdiagnostic modules (as well as subsequent selections of diagnosticmodules) may be selected based on the access credentials of the contentexecutor. For instance, the access credentials of a particularclinician, which may be based the clinician's professional credentials,experience, etc., may be used to determine the available diagnosticmodules that may or may not be executed, monitored, and scored by theparticular clinician.

Steps 904-906, which may be performed for each of the diagnostic modules(or assessments) in the initial battery, correspond to the execution ofthe assessment and the receiving of responses/feedback from the receiverdevice 740. In step 904, the content execution device 710 may initiatethe execution of one or more of the assessments selected in steps901-903. As discussed above, the context executor may initiate theexecution of the diagnostic modules using the execution device 710, andbased on the Bluetooth pairing or other network connection establishedbetween the execution device 710 and the receiver device 740, along withthe corresponding communications between the active client applicationsrunning on the execution device 710 and the receiver device 740, theexecution of the assessment on the execution device 710 may causeindividual interactive assessment components (e.g., questions or otherdiagnostic/assessment content) to be transmitted to and rendered on thereceiver device 740.

In step 905, during an execution session for a particular assessment,the execution device 710 may continuously monitor and/or modify theexecution process as experienced at the receiver device 740. Forexample, in some embodiments, the content executor may be authorized toterminate an assessment early, restart an assessment, or navigate todifferent sections/questions of an assessment during the execution. Suchmodifications may be performed by the execution device 710, in responseto commands from the content executor or automatically based onreal-time evaluations of the responses/feedback received from thecontent recipient (or examinee). For example, an in-progress assessmentexecution may be automatically terminated early by the execution device710, based on a determination of a passing or failing score or based onreal-time computation of diagnostic algorithm(s) identifying probabilityof presence or absence of specific expression(s) of disorder(s) inquestion made before the end of the assessment. Likewise, if theresponse data indicates that the examinee has become distracted orconfused during the course of the assessment, based on responses and/oradditional data such as response times, facial expression, eye movement,etc., then the execution device 710 may automatically re-start theassessment or return to the point in the assessment at which theexaminee's comprehension diverged. Additionally, in some embodiments,some of the response/feedback data generated during the execution of anassessment may be the responses or feedback of the content executor(rather than the examinee), for example, based on interactions with orobservations of examinee during the assessment. Such responses orfeedback may include verbal responses or feedback provided by theexaminee to the content executor/clinician, behavioral or emotionalcues, etc.

In step 906, the results of the assessment (e.g., responses, scores,and/or any additional examinee data or environmental data collectedduring the execution) may be transmitted to and stored by the executiondevice 710. The transmission and storage of assessment results may occuronly after each assessment (e.g., test, subtest, or diagnostic module)is completely, or may occur one or more times during assessmentexecutions, such as after each assessment question/response or aftereach section is completed, etc.

After each of the diagnostic modules/assessments in the initial batteryhave been executed and evaluated in steps 904-906, the execution device710 may transmit the results (e.g., assessment scores and/or other userfeedback or relevant data points) to the diagnostic analyzer server 720in step 907. As discussed above, the diagnostic analyzer server 720 mayanalyze the assessment results (and/or any additional context data suchas facial expressions or other observations of the examinee, orenvironmental conditions associated with the assessment), and thenselect one or more additional diagnostic modules (or assessments) forexecution during the clinical diagnosis of the examinee.

For instance, in some embodiments, the diagnostic analyzer server 720may compare the scores collected for the content recipient, from theinitial set of executed assessments, to one or more diagnostic profiles.Referring briefly to FIGS. 12A-12B, two example charts 1200 are shownillustrating mappings of sets of score to several different diagnosticprofiles. FIG. 12A is a generic mapping between characteristics anddiagnoses, and FIG. 12B is a specific example mapping relating todyslexia and other learning disorders. Such mappings may be used, alongwith the additional data analyses described below, to identify one ormore possible diagnoses for the content recipient, based on theassessment scores. Additionally, as described below, the mappings may beused to help select additional diagnostic modules/assessments forrecommendation by the diagnostic analyzer 720.

In FIG. 13, an example user interface 1300 is shown including graphicalrepresentations of a set of assessment scores for a particular contentrecipient (“Sample Child”), along with a corresponding analyses thatidentifies which possible diagnoses (e.g., dyslexia and/or learningdisorders) are consistent with the assessment scores for the examinee.Finally, in FIG. 14, another example user interface 1400 is shownincluding an additional set of diagnostic modules that have beenselected by the diagnostic analyzer server 720 for subsequent executionby the execution device 710 during the clinical diagnosis of theexaminee. These diagnostic modules selected by the diagnostic analyzerserver 720 may be selected based on the above determinations of thepossible and/or probable diagnoses for the examinee. As shown in thisexample, the additional diagnostic modules selected by the diagnosticanalyzer server 720 are presented within the “Instrument Library”section of the user interface 1401, so that the content executor maydrag selections of diagnostic modules from the library 1401 to the“Assessment Battery” section of the user interface 1302 to initiatetheir execution during the clinical diagnosis session of the examinee.

As illustrated above, the diagnostic analyzer server 720 may selectadditional assessments for execution based on the examinee's previousassessment scores, and particularly by a determination of whichdiagnoses are consistent with the previous assessment scores. Additionalexamples of the various techniques, data sources, and algorithms thatthe diagnostic analyzer server 720 may use to select additionaldiagnostic modules to be executed for the examinee during the clinicaldiagnosis are described below in more detail with reference to FIG. 10.Finally, after the diagnostic analyzer server 720 determines theadditional assessments, data identifying the assessments (and/or theassessment software objects themselves) may be transmitted from thediagnostic analyzer server 720, and received by the execution device 710in step 908.

Referring now to FIG. 10, a flow diagram is shown illustrating a processof analyzing the execution results of diagnostic modules for aparticular content recipient (or examinee), and/or additional datarelated to the content recipient from various data sources, andselecting a subsequent set of diagnostic modules for execution during aclinical diagnostic session for the content recipient. As describedbelow, the steps in this process may be performed by one or morecomponents in the diagnostic and content selection system 700 describedabove. For example, each of the steps 1001-1007 may be performed by adiagnostic analyzer server 720 in communication with a content executiondevice 710 (and/or a receiver devices 740) executing the assessmentcontent and transmitting results to the diagnostic analyzer server 720.However, in other examples, some or all of the steps 1001-1007 may beperformed directly by or in conjunction with an execution device 710and/or receiver device 740, and thus the analyzer server 720 may beoptional in such embodiments. It should also be understood that thevarious features and processes described herein, including receiving andanalyzing the results of diagnostic assessments, and selectingadditional assessments/diagnostic modules for examinees, need not belimited to the specific systems and hardware implementations describedabove in FIGS. 1-7.

In step 1001, a diagnostic analyzer server 720 may receive a set ofexecution results for one or more sets of diagnostic modules, where theexecution results identify and/or are associated with a particularcontent recipient. As discussed above, in certain embodiments thediagnostic modules may be software modules containing interactivecontent resources to execute clinical assessments and collect responsedata from an examinee. In some embodiments, the execution of thediagnostic modules may be initiated by a content executor user (e.g., aclinician, teacher, or medical professional) on a content executiondevice 710, which may cause the interactive content to be transmitted toand displayed on a separate content receiver device 740 operated by theexaminee (e.g., a student or patient). In such cases, the executiondevice 710 may collect the assessment results and/or user responses orfeedback from the receiver device 740, and transmit them back to thediagnostic analyzer server 720 in step 1001. Depending on the particularsystem implementation, the diagnostic results received in step 1001 maycorrespond to one or more assessments (e.g., subtests, tests, or groupsof tests), or may correspond to user responses/feedback to individualassessment items (e.g., a single question). Additionally, as discussedabove, the data received in step 1001 need not be limited only toexaminee answers of assessment questions, but also may includeadditional user responses/feedback such as the notes or observations ofthe clinician, comments from the examinee, facial expression or bodylanguage data of the examinee, etc. Additional data received in step1001 may include optional additional data related to the execution ofthe diagnostic modules, such as data identifying the content executor,specifications of the execution device 710 and/or receiver device 740,execution time or location, environmental conditions associated with theassessment such as movement, background noise, temperature, etc.

In step 1002, the diagnostic analyzer server 720 may receive from one ormore additional data sources (e.g., servers other than the diagnosticanalyzer server 720, execution device 710, or receiver device 740)additional data relating to the examinee. The additional data receivedin step 1002 may include, for example, an initial (pre-testing)diagnosis of the examinee from a parent, teacher, psychologist or othermedical professional, or other individual familiar with the examinee.Such pre-testing diagnosis may take the form of a list of concerns orbehavior characteristics about the examinee based on direct observation,a hypothesis as to the clinical diagnosis of the examinee, and/or anyother comments or relevant data points about the examinee. Additionaldata received in step 1002 may include any previous assessment results,academic records, medical or educational records of the examinee, familyhistory data, or any other relevant data points that the diagnosticanalyzer server 720 may use to determine potential diagnoses, computeprobabilities, and select diagnostic modules for the examinee.

In step 1003, the diagnostic analyzer server 720 may initiate areal-time (or near real-time) analysis of the clinical diagnosticresults data received in step 1001 and/or the additional examinee datareceived in 1002. As discussed below in more detail, the analysis instep 1003 may initiate processes by which one or more potentialdiagnoses are determined for the examinee, probabilities are calculatedfor each potential diagnoses, and additional diagnostic modules areselected to confirm or refute the potential diagnoses for the examinee.In some embodiments, the analysis in step 1003 may be initiatedimmediately in response to the diagnostic analyzer server 720 receivinga new set of diagnostic results (step 1001) or receive additionalexaminee data from an external data source (step 1002). As noted in thisexample, the analysis initiated in step 1003 may be performed in realtime (or near real time), thereby allowing the diagnostic analyzerserver 720 to select and transmit the additional diagnosticmodules/assessments to execution devices 710 so that a clinicalassessment may continue uninterrupted and without significant delay tothe clinician or examinee. However, in other examples, the analysisinitiated in step 1003 need not be performed in real time in response tonew data received in steps 1001-1002, but may be initiated only afterreceiving a threshold amount of diagnostic results (e.g., a completedassessment or battery), or may be initiated in accordance with ascheduling process on the diagnostic analyzer server 720.

Additionally, although this example shows that data may be received viastep 1001 and step 1002, in other examples the analysis in step 1003 maybe initiated based only on a set of diagnostic results received in step1001, without receiving or having any additional data relating to theexaminee (e.g., without step 1002), or vice versa, where the analysis instep 1003 may be initially performed based on available data relating tothe examinee but without any diagnostic results (e.g., without step1001). In some embodiments, the analysis in step 1003 may be triggeredor initiated by a process/service running continuously on the diagnosticanalyzer server 720, so that an updated analysis in step 1003 may beautomatically triggered whenever any new data is received via step 1001or step 1002.

In step 1004, the diagnostic analyzer server 720 may determine one ormore potential diagnoses for the examinee, based on the data diagnosticresults data received for the examinee in steps 1001 and/or theadditional examinee data received in step 1002. The diagnostic analyzerserver 720 also may calculate probabilities and/or rankings for thepotential diagnoses in step 1004. In some embodiments, the determinationof potential diagnosis for the examinee may include mapping theexaminee's assessment results to one or diagnoses consistent with thoseresults. For instance, referring again to the mappings of FIGS. 12A-12B,each diagnostic module/assessment may correspond to one or morecharacteristics (in FIG. 12A) or constructs (in FIG. 12B), and themappings 1200 may be used to determine to what level are the examinee'sassessment scores/results consistent as well as those inconsistent(e.g., positive and negative diagnostic indicators) with the diagnoseslisted on the top lines.

To illustrate the mapping of constructs (or characteristics, skills,etc.) to potential diagnoses, the example mapping 1200 b in FIG. 12B maybe used to identify potential diagnoses of dyslexia or otherreading/learning disorders. Using this example, if an execution of aninitial set diagnostic modules/assessments for an examinee reveals thatthe examinee's listening comprehension skills are adequate, but his/herword recognition skills are weak, the diagnostic analyzer server 720 mayuse mapping 1200 b in FIG. 12B to determine that these diagnosticresults are entirely consistent with both orthographic and mixeddyslexia. Accordingly, the diagnostic analyzer server 720 may designateorthographic and mixed dyslexia as the highest probability or highestranked potential diagnoses. Additionally, the examinee's results areentirely consistent with phonological dyslexia, but only have a singlematching data point instead of two. Thus, phonological dyslexia may beassigned a probability or ranking just below the orthographic and mixeddyslexia in this example. Further, although the examinee's results arenot entirely consistent with a language disorder or a global readingdisorder, there is one data point of consistency with these diagnosis,and it is possible that additional listening comprehension assessmentsmay in fact reveal that the examinee has weak (rather than adequate)listening comprehension skills. Therefore, the diagnostic analyzerserver 720 may determine that the potential diagnoses of a languagedisorder or a global reading disorder should be assigned a probabilityor ranking below the three dyslexia diagnoses, followed by the languagedisorder diagnosis which has no points of consistency and one point ofinconsistency with the examinee's assessment results. Finally, a readingcomprehension disorder diagnosis may be assigned the lowest ranking orprobability in this example, because it has no points of consistency andtwo points of inconsistency with the examinee's assessment results.Thus, in this example, the analyzer server 720 may potentially selectone or more Decoding assessments and/or Spelling by Dictationassessments to be administered by a clinician, in order increase theprobability of mixed dyslexis and refine the diagnosis. Additionally,further embodiments may determine the impact of supporting abilities andbehaviors in the expression of mixed diagnostic profiles (e.g.attentional, memory, executive, emotional, etc. . . . disturbances).

As noted above, a single diagnostic module may be executed to assess theexaminee's proficiencies with respect to a single construct (orcharacteristic, skill, trait, etc.). In other examples, a singlediagnostic module may yield assessment results for the examinee undermultiple constructs. Additionally, in some embodiments, many differentassessments may be available within the content resource library toassess a single construct. For example, it should be understood thatdozens or even hundreds of different diagnostic assessments may bepublished by various different content providers to assess a student'sreading comprehension, word recognition, spelling proficiency, etc.Thus, both the logic within the diagnostic analyzer server 720, as wellas the content executor, may determine that several differentassessments should be executed for a particular construct, in order toperform a more thorough clinical diagnostic for the particularconstruct. Additionally, although this example shows only broadclassifications for different constructs (e.g., Adequate, Weak), inother examples, assessment results may take the form of letter grades ornumerical scores (e.g., 1-100), color gradients illustrating risk level,likelihood ratios, or the like, which may be compared to differentvalues or ranges of values within the mappings for different diagnoses.

Alternatively, or in addition to, comparing assessment results todiagnoses mappings as described above, the diagnostic analyzer server720 also may determine the probabilities and/or rankings for potentialdiagnoses in step 1004 based on statistical or analytics data relatingto the potential diagnoses. For example, if the examinee's assessmentresults are consistent with multiple different diagnoses (based on acomparison of the assessment results to constructs/characteristics),then the diagnostic analyzer server 720 may retrieve data from one ormore external data sources (e.g., a response-diagnosis analytics datastore 725) to determine overall statistical probability associated witheach of the potential diagnosis. Referring again to the example mapping1200 b, if the examinee's initial assessment results were consistentwith all three types of dyslexia and/or other learning disorders, thendiagnostic analyzer server 720 may assign the probabilities/rankingsbased on the prevalence of each disorder. In some embodiments, thediagnostic analyzer server 720 may use the overall prevalence rates of adiagnosis across the general population, while in other cases subsetprevalence rates applicable to the examinee's profile (e.g., prevalenceby age, grade, gender, geographic region, having a particular medicalcondition, etc.) may be used. Further, the diagnostic analyzer server720 may consider additional factors when determining the diagnosisprobabilities/rankings in step 1004, such as weighting theprobabilities/rankings based on an initial (pre-assessment) diagnosis ofthe examinee from a parent, teacher, psychologist, etc., or based ondirect observations of the examinee recorded by the content executorduring the assessment. Additional factors in calculating theprobabilities/rankings for the diagnoses may include the examinee'sprevious assessment results, academic records, medical or educationalrecords, family history data, or any other relevant data points.

In step 1005, based on the probabilities/rankings for the differentdiagnoses determined in step 1004, the diagnostic analyzer server 720may select a plurality of additional diagnostic content (e.g.,assessments) to be presented to the examinee during the clinicaldiagnostic session. In some embodiments, the selection ofassessments/diagnostic modules in step 1005 may be targeted based on thehighest probability diagnosis determined in step 1004. For instance, ifstep 1004 determined a highest-probability diagnosis of orthographicdyslexia for the examinee, the assessments selected in step 1005 may bethose assessments that specifically target orthographic dyslexia,including assessments that may attempt to definitely confirm or rule-outthat diagnosis, or to definitely confirm or rule-out particularconstructs or characteristics associated with that diagnosis. Incontrast, assessments that only target the lower probability/rankingdiagnoses from step 1004 (or the constructs/characteristics associatedwith those diagnoses) might not be selected in step 1005. Further, insome embodiments, rather than limiting the selection in step 1005 toassessments associated with the single highest probability/rankingdiagnosis, assessments may be selected that are associated with severalof the high probability/ranking diagnoses (e.g., the top 2, 3, 4, . . ., N, diagnoses, or any diagnosis with a probability greater than aparticular threshold, etc.). Additionally, in some cases, assessmentsthat are relevant to multiple of the high probability/ranking diagnoses(e.g., a series of oral reading assessments which are relevant tomultiple potential high-probability diagnoses) may be weighted more thanassessments that are relevant to only a single high probability/rankingdiagnosis. Instances not identifying a single high probability diagnosiswithin the initial expected range of diagnosis may direct the examineeto a new set of protocols and most probable diagnostic cluster(s) (e.g.,ADHD).

In step 1006, the diagnostic analyzer server 720 may rank (or cull) theplurality of additional diagnostic content (e.g., assessments) selectedin step 1005, thereby determining a smaller subset of assessments to betransmitted and presented to the content executor and/or examinee. Thesubset of assessments/diagnostic modules may be selected in step 1006based on any number of algorithms for determining the most valuableassessments to be executed for and/or by the examinee as part of theexaminee's clinical diagnostic session. Such algorithms, severalexamples of which are described below, may be designed with the goals ofexpediting the clinical diagnostic session of the examinee (e.g., byexecuting more relevant and revealing assessments earlier in thesession, and by not selecting irrelevant or redundant assessments forexecution), and improving the overall end product of the clinicaldiagnostic session (e.g., achieving higher statistical likelihoods forpotential diagnoses, and more definitely ruling out unlikely diagnoses).

In some embodiments, the value of an assessment may be determined instep 1006 as a measurement of the magnitude of the likely change (eitherpositive or negative) in the probabilities of the highest probabilitydiagnoses, that will result in the execution of the assessment at thispoint in the examinee's clinical diagnostic session. In other words,assessments that are likely to cause a large increase in the probabilityof one or more diagnoses (e.g., helping to confirm those diagnoses)and/or a large decrease in the probability of one or more diagnoses(e.g., helping to rule-out those diagnoses) will be selected as morevaluable assessments at this point in the diagnostic session, over otherassessments that likely to cause a relatively small increases and/ordecreases in the probabilities of the diagnoses (e.g., providing lesshelp in either confirm or ruling out those diagnoses). Thus, for eachpotential assessment evaluated for selection in step 1006, acorresponding measurement of the likely magnitude change (or delta) maybe calculated and/or retrieved for each of the potential diagnosesassociated with the examinee. As an example, referring again to mapping1200 b, for a particular assessment within the reading comprehensionfield, the diagnostic analyzer server 720 may determine in step 1006that the execution of the assessment is likely to change the probabilitya phonological dyslexia diagnosis by A %, the probability anorthographic dyslexia diagnosis by B %, the probability an mixeddyslexia diagnosis by C %, and so on. In other words, each of thesepercentage magnitudes answers the question: if Assessment A is executedat this point in the examinee's clinical diagnostic session, is theconfidence level in Diagnosis B likely to be higher or lower, and by howmuch? In some embodiments, only the magnitude of the likely change maybe measured, and thus a likely change of −15% for a diagnosis isconsidered to be equally valuable as a likely change of +15%. While inother cases, a likely positive change in the diagnosis may be weightedas more valuable by the diagnostic analyzer server 720, or vice versa.

The algorithms and other techniques used to determine the magnitude ofchanges in the probabilities of the particular diagnoses that are likelyto result from the execution of the particular assessment, may be basedon a number of factors including statistical and/or analytical dataretrieved from the response-diagnosis analytics data store 725.

A first factor used in computing the likely change in the probabilitydelta of a diagnosis that will result from executing an assessment, maybe a calculation in the confidence level in examinee's projected resultsfor the assessment. This confidence level may be calculated usinganalytics data retrieved from the response-diagnosis analytics datastore 725, based on the examinee's previous results on relatedassessments. For example, if the examinee has taken three previousassessment batteries in the field of written expression, and receivedconsistent results on those assessment batteries, the response-diagnosisanalytics data store 725 may determine that the results of thoseassessment batteries are highly predictive of what the examinee islikely to score on another particular written expression assessment.However, in other cases, the analytics analysis of the user's previousassessments may determine that the examiner's performance on a differentselected assessment cannot be predicted with a high level of confidence.The analytics used to make such predictions may be based on a trainedmachine learning algorithm that analyses the examinee's pattern ofassessment results against a data store of results from other examinees.The correlations and predictive abilities of certain assessment results(or combinations of results) to calculate the likely results ofadditional assessments may be determined within a particular construct,or there may be correlations/predictions based on assessments acrossconstructs. Further, within a particular characteristic or construct,the analytics analysis may determine that a certain assessment (orcombination of assessments) are highly predictive of some additionalassessments, but are less predictive (or not at all predictive) of otheraddition assessments.

A second factor used in computing the likely change in the probabilitydelta of a diagnosis that will result from executing an assessment, maybe a calculation of the projected diagnosis probability, assuming thatthe examinee's results for the assessment are the anticipated results(e.g., the results anticipated based on the calculations in the firstfactor). In other words, even if the examinee performs as expected on anadditional assessment, by what amount will that new score change theoverall probability of the diagnosis. As an example, if an examinee hasalready received thorough testing within a particular construct area,even if the examination results are thus far conflicted, then anadditional assessment battery that same construct area may have littlechange the overall probability of the diagnosis regardless of whatresults are received. As another example, depending on which diagnosesare currently ranked as the highest probability diagnoses, clinicaltesting within certain construct areas might not be particularlyrelevant to those diagnoses, regardless of what results are receivedfrom the assessments. In both of these cases, the likely change in theprobability delta for the selected assessments/batteries may be small,even if the examinee's projected scores selected assessments/batteriescannot be predicted with a high level of confidence (e.g., using thecalculation of first factor). The calculations associated with thesecond factor in this example (e.g., updated diagnosis probabilitychanges based on assumed assessment results) also may be performed usingdata from the response-diagnosis analytics data store 725, and mayinvolve similar or identical calculations to the initial diagnosisprobability/ranking calculations discussed above in step 1004.

Using the above calculations, each of the assessments and/or diagnosticmodules initially selected in step 1005 may be scored, and thenranked/culled to select the subset of assessments/diagnostic moduleshaving the highest anticipated probability deltas for the current set ofdiagnoses. In other words, the subset of assessments/diagnostic modulesselected in step 1006 may be those assessments/diagnostic modules thatare most likely to result in larger increases and/or decreases in theprobabilities of the higher ranking diagnoses (e.g., helping to eitherconfirm or rule-out those diagnoses), rather than theassessments/diagnostic modules that are likely to result in smallerincreases/decreases in these probabilities (e.g., providing less help ineither confirm or ruling out those diagnoses).

The assessment subset selection in step 1006 may be based on furtheradditional factors as well, such as the execution time or cost of anassessment (e.g., favoring lower cost and time assessments so that moreoverall assessments may be executed during the diagnostic session). Insome embodiments, the number of additional assessments/diagnosticmodules selected in step 1006 may be based on an anticipated executiontime of the selected content (e.g., 30 mins, 1 hour, etc.), which may beprovided by the content executor (e.g., clinician) or preset by thediagnostic analyzer server 720. Additionally, the assessments/diagnosticmodules may be filtered out based on the authorization credentials ofeither the examinee and/or the content executor (e.g., clinician), orbased on the location-based content restrictions (e.g., when certainassessments are preferred, not preferred, or invalid within certainjurisdictions, etc.).

Finally, in step 1007, the subset of assessments/diagnostic modulesselected in step 1006 may be packaged and/or transmitted to the contentexecutor device 710 for execution during the clinical diagnostic sessionas described above. In some cases, the diagnostic analyzer server 720may package multiple selected assessments into customized diagnosticmodules before transmission, thereby allowing the diagnostic analyzerserver 720 to select transmit only the most valuable assessment parts(e.g., tests or subtests), so that the less valuable assessment parts(e.g., tests or subtests) need not be executed during the session.

A limited number of embodiments of the present invention have beendescribed, but the invention contemplates many more that are enabled bythe description. For example, the concepts of the invention areapplicable to performing analyses and selections of assessments as wellas any other types of interactive content resources, including mediaresources, professional training and educational resources, interactivegaming resources, interactive eCommerce resources, etc. A number ofvariations and modifications of the disclosed embodiments can also beused. Specific details are given in the above description to provide athorough understanding of the embodiments. However, it is understoodthat the embodiments may be practiced without these specific details.For example, well-known circuits, processes, algorithms, structures, andtechniques may be shown without unnecessary detail in order to avoidobscuring the embodiments.

Implementation of the techniques, blocks, steps and means describedabove may be done in various ways. For example, these techniques,blocks, steps and means may be implemented in hardware, software, or acombination thereof. For a hardware implementation, the processing unitsmay be implemented within one or more application specific integratedcircuits (ASICs), digital signal processors (DSPs), digital signalprocessing devices (DSPDs), programmable logic devices (PLDs), fieldprogrammable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described above, and/or a combination thereof.

Also, it is noted that the embodiments may be described as a processwhich is depicted as a flowchart, a flow diagram, a swim diagram, a dataflow diagram, a structure diagram, or a block diagram. Although adepiction may describe the operations as a sequential process, many ofthe operations can be performed in parallel or concurrently. Inaddition, the order of the operations may be re-arranged. A process isterminated when its operations are completed, but could have additionalsteps not included in the figure. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination corresponds to a return ofthe function to the calling function or the main function.

Furthermore, embodiments may be implemented by hardware, software,scripting languages, firmware, middleware, microcode, hardwaredescription languages, and/or any combination thereof. When implementedin software, firmware, middleware, scripting language, and/or microcode,the program code or code segments to perform the necessary tasks may bestored in a machine readable medium such as a storage medium. A codesegment or machine-executable instruction may represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, asoftware package, a script, a class, or any combination of instructions,data structures, and/or program statements. A code segment may becoupled to another code segment or a hardware circuit by passing and/orreceiving information, data, arguments, parameters, and/or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, etc.

For a firmware and/or software implementation, the methodologies may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine-readable mediumtangibly embodying instructions may be used in implementing themethodologies described herein. For example, software codes may bestored in a memory. Memory may be implemented within the processor orexternal to the processor. As used herein the term “memory” refers toany type of long term, short term, volatile, nonvolatile, or otherstorage medium and is not to be limited to any particular type of memoryor number of memories, or type of media upon which memory is stored.

Moreover, as disclosed herein, the term “storage medium” may representone or more memories for storing data, including read only memory (ROM),random access memory (RAM), magnetic RAM, core memory, magnetic diskstorage mediums, optical storage mediums, flash memory devices and/orother machine readable mediums for storing information. The term“machine-readable medium” includes, but is not limited to portable orfixed storage devices, optical storage devices, and/or various otherstorage mediums capable of storing that contain or carry instruction(s)and/or data.

While the principles of the disclosure have been described above inconnection with specific apparatuses and methods, it is to be clearlyunderstood that this description is made only by way of example and notas limitation on the scope of the disclosure.

What is claimed is:
 1. A system, comprising: a content execution device,including: a processing unit comprising one or more processors; aplurality of network interfaces; and memory coupled with and readable bythe processing unit and storing therein a set of instructions which,when executed by the processing unit, causes the execution device to:receive a selection of diagnostic modules for execution on a contentreceiver device; initiate execution of the selected diagnostic modulesby transmitting first interactive content within particular diagnosticmodules to the content receiver device; after the execution of theselected diagnostic modules, determine performance measurements for theselected diagnostic modules based on received responses to theinteractive content, wherein the performance measurements are associatedwith a recipient of the first interactive content; transmit thedetermined performance measurements to a diagnostic analyzer server;receive selections of a subsequent diagnostic module from the diagnosticanalyzer server; and initiate execution of the subsequent diagnosticmodule by transmitting second interactive content to the contentreceiver device; the diagnostic analyzer server comprising: a processingunit comprising one or more processors; and memory coupled with andreadable by the processing unit and storing therein a set ofinstructions which, when executed by the processing unit, causes thediagnostic analyzer server to: receive the determined performancemeasurements from the content execution device; retrieve apreviously-collected entity record associated with the recipient;determine the subsequent diagnostic module based on the determinedperformance measurements and the previously-collected entity record; andtransmit an identification of the subsequent diagnostic module to thecontent execution device.
 2. The system of claim 1, wherein the memoryof the diagnostic analyzer stores additional instructions that, whenexecuted by the processing unit, causes the diagnostic analyzer serverto determine the subsequent diagnostic module by: determining aplurality of possible diagnoses for the recipient based on thedetermined performance measurements and the previously-collected entityrecord; determining a probability ranking for the plurality of possiblediagnoses; and determining the subsequent diagnostic module based on ahighest probability diagnosis for the recipient.
 3. The system of claim2, wherein determining the probability ranking for the plurality ofpossible diagnoses comprises: determining an initial probability rankingfor the plurality of possible diagnoses based on input received beforethe execution of the selected diagnostic modules; and revising theinitial probability ranking based on the determined performancemeasurements.
 4. The system of claim 2, wherein determining theprobability ranking for the plurality of possible diagnoses comprisesdetermining a probability for each of the plurality of possiblediagnoses using a trained machine learning algorithm.
 5. The system ofclaim 2, wherein determining the subsequent diagnostic module comprises:analyzing a plurality of diagnostic modules, each comprising one or moreinteractive content resources; calculating, for each particularinteractive content resource in each of the diagnostic modules, aprojected amount of change to the probability of the highest probabilitydiagnosis, resulting from the execution of the particular interactivecontent resource; and determining the subsequent diagnostic module basedon the calculated projected amounts of change to the probabilities. 6.The system of claim 5, wherein the projected amounts of change to theprobability of the highest probability diagnosis are computed based onanalytics data correlating probabilities for a plurality of diagnoses toa plurality of responses from a plurality of additional contentrecipients to the selected diagnostic modules.
 7. The system of claim 1,wherein the determination of the subsequent diagnostic module comprises:determining one or more geographic regions associated with therecipient; and determining the subsequent diagnostic module based on theone or more geographic regions.
 8. A method, comprising: initiatingexecution of a selected diagnostic module by transmitting firstinteractive content to a content receiver device; determiningperformance measurements for the selected diagnostic module based onreceived responses to the first interactive content, wherein theperformance measurements are associated with a recipient of the firstinteractive content; retrieving a previously-collected entity recordassociated with the recipient; determining a subsequent diagnosticmodule based on the determined performance measurements and thepreviously-collected entity record; and initiating execution of thesubsequent diagnostic module by transmitting second interactive contentto the content receiver device.
 9. The method of claim 8, whereindetermining the subsequent diagnostic module includes: determining aplurality of possible diagnoses for the recipient based on thedetermined performance measurements and the previously-collected entityrecord; determining a probability ranking for the plurality of possiblediagnoses; and determining the subsequent diagnostic module based on ahighest probability diagnosis for the recipient.
 10. The method of claim9, wherein determining the probability ranking for the plurality ofpossible diagnoses comprises: determining an initial probability rankingfor the plurality of possible diagnoses based on input received beforethe execution of the selected diagnostic module; and revising theinitial probability ranking based on the determined performancemeasurements.
 11. The method of claim 9, wherein determining theprobability ranking for the plurality of possible diagnoses comprisesdetermining a probability for each of the plurality of possiblediagnoses using a trained machine learning algorithm.
 12. The method ofclaim 9, wherein determining the subsequent diagnostic module comprises:analyzing a plurality of diagnostic modules, each comprising one or moreinteractive content resources; calculating, for each particularinteractive content resource in each of the diagnostic modules, aprojected amount of change to the probability of the highest probabilitydiagnosis, resulting from the execution of the particular interactivecontent resource; and determining the subsequent diagnostic module basedon the calculated projected amounts of change to the probabilities. 13.The method of claim 12, wherein the projected amounts of change to theprobability of the highest probability diagnosis are computed based onanalytics data correlating probabilities for a plurality of diagnoses toa plurality of responses from a plurality of additional contentrecipients to the selected diagnostic module.
 14. The method of claim 8,wherein determining the subsequent diagnostic module comprises:determining one or more geographic regions associated with therecipient; and determining the subsequent diagnostic module based on theone or more geographic regions.
 15. A method, comprising: displaying afirst diagnostic module content on a content receiver device;determining performance measurements based on received responses from arecipient of the first diagnostic module content; retrieving apreviously-collected data record associated with the recipient;determining a second diagnostic module content based on the performancemeasurements and the data record; and displaying the second diagnosticmodule content on the content receiver device.
 16. The method of claim15, wherein determining the second diagnostic module content includes:determining a plurality of possible diagnoses for the recipient based onthe determined performance measurements; determining a probabilityranking for the plurality of possible diagnoses; and determining thesecond diagnostic module content using the probability ranking.
 17. Themethod of claim 16, wherein determining the probability ranking for theplurality of possible diagnoses comprises: determining an initialprobability ranking for the plurality of possible diagnoses; andrevising the initial probability ranking based on the determinedperformance measurements.
 18. The method of claim 17, whereindetermining the initial probability ranking comprises determining aprobability for each of the plurality of possible diagnoses using atrained machine learning algorithm.
 19. The method of claim 17, whereindetermining the second diagnostic module content comprises: determininga content relationship between the first diagnostic module content andthe second diagnostic module content; and determining the seconddiagnostic module content using the content relationship.
 20. The methodof claim 15, wherein determining the second diagnostic module contentcomprises: determining an attribute associated with an administratorthat caused the first diagnostic content to be displayed on the contentreceiver device; and determining the second diagnostic module contentbased on the attribute associated with the administrator.